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    "# Classification of Forest Type Using Geographic Data\n",
    "### Analysis by Max Calabro\n",
    "\n",
    "This data set is a collection of observations of forest type (Spruce/Fir, Aspen, Lodgepole Pine, etc) in four wilderness areas in Colorado. The areas are west of Fort Collins near Rocky Mountain National Park, and were chosen because they have had minimal human disturbance. The data set includes 581,012 examples (each one a 30m by 30m square plot of forest) and 54 features describing physical attributes of those plots. Ten of these features are quantitative measurements like elevation, slope and aspect, and the remaining 44 come from two classification features (wilderness area and soil type) which have been one hot encoded ahead of time by the data set creators.\n",
    "\n",
    "A full description of the data set can be found here: https://archive.ics.uci.edu/ml/datasets/covertype. \n",
    "\n",
    "The data set was originally used in 1998 to compare a linear model and a neural network. With those models, they were able to achieve 58% and 70% accuracy on a test set, respectively. Twenty years later, we should be able to better and get there significantly faster. In this analysis, we won't look at neural networks specifically, but instead will model with Logistic Regression, Naive Bayes, K-Nearest Neighbors, Support Vector Machine, Decision Tree, Random Forest, and Gradient Boosting. Our primary tools will be numpy and pandas for data handling, matplotlib and seaborn for visualization, scikit-learn for modeling and analysis, and of course this Jupyter notebook for presentation.\n",
    "\n",
    "The original owners of the data set are:\n",
    "\n",
    "\t\tRemote Sensing and GIS Program\n",
    "\t\tDepartment of Forest Sciences\n",
    "\t\tCollege of Natural Resources\n",
    "\t\tColorado State University\n",
    "\t\tFort Collins, CO  80523\n",
    "\n",
    "The fact that they've made this data set publicly available is greatly appreciated."
   ]
  },
  {
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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   "source": [
    "# Using the documentation, let's name our columns appropriately. We have 10 quantitative, numeric features,\n",
    "# followed by 4 qualitative binary features for wilderness area, 40 qualitative binary features for soil type,\n",
    "# and finally our outcome variable, 'Cover', which is an integer 1-7.\n",
    "\n",
    "soil_types = ['Soil_Type_%.2d' %x for x in range(1, 41)]\n",
    "\n",
    "col_names = ['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology', \n",
    "             'Horizontal_Distance_To_Roadways', 'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm', \n",
    "             'Horizontal_Distance_To_Fire_Points', 'Wild_Area_1', 'Wild_Area_2', 'Wild_Area_3', 'Wild_Area_4']\n",
    "\n",
    "col_names = col_names + soil_types + ['Cover']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Aspect</th>\n",
       "      <th>Slope</th>\n",
       "      <th>Horizontal_Distance_To_Hydrology</th>\n",
       "      <th>Vertical_Distance_To_Hydrology</th>\n",
       "      <th>Horizontal_Distance_To_Roadways</th>\n",
       "      <th>Hillshade_9am</th>\n",
       "      <th>Hillshade_Noon</th>\n",
       "      <th>Hillshade_3pm</th>\n",
       "      <th>Horizontal_Distance_To_Fire_Points</th>\n",
       "      <th>...</th>\n",
       "      <th>Soil_Type_32</th>\n",
       "      <th>Soil_Type_33</th>\n",
       "      <th>Soil_Type_34</th>\n",
       "      <th>Soil_Type_35</th>\n",
       "      <th>Soil_Type_36</th>\n",
       "      <th>Soil_Type_37</th>\n",
       "      <th>Soil_Type_38</th>\n",
       "      <th>Soil_Type_39</th>\n",
       "      <th>Soil_Type_40</th>\n",
       "      <th>Cover</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2596</td>\n",
       "      <td>51</td>\n",
       "      <td>3</td>\n",
       "      <td>258</td>\n",
       "      <td>0</td>\n",
       "      <td>510</td>\n",
       "      <td>221</td>\n",
       "      <td>232</td>\n",
       "      <td>148</td>\n",
       "      <td>6279</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2590</td>\n",
       "      <td>56</td>\n",
       "      <td>2</td>\n",
       "      <td>212</td>\n",
       "      <td>-6</td>\n",
       "      <td>390</td>\n",
       "      <td>220</td>\n",
       "      <td>235</td>\n",
       "      <td>151</td>\n",
       "      <td>6225</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2804</td>\n",
       "      <td>139</td>\n",
       "      <td>9</td>\n",
       "      <td>268</td>\n",
       "      <td>65</td>\n",
       "      <td>3180</td>\n",
       "      <td>234</td>\n",
       "      <td>238</td>\n",
       "      <td>135</td>\n",
       "      <td>6121</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2785</td>\n",
       "      <td>155</td>\n",
       "      <td>18</td>\n",
       "      <td>242</td>\n",
       "      <td>118</td>\n",
       "      <td>3090</td>\n",
       "      <td>238</td>\n",
       "      <td>238</td>\n",
       "      <td>122</td>\n",
       "      <td>6211</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2595</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>153</td>\n",
       "      <td>-1</td>\n",
       "      <td>391</td>\n",
       "      <td>220</td>\n",
       "      <td>234</td>\n",
       "      <td>150</td>\n",
       "      <td>6172</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Elevation  Aspect  Slope  Horizontal_Distance_To_Hydrology  \\\n",
       "0       2596      51      3                               258   \n",
       "1       2590      56      2                               212   \n",
       "2       2804     139      9                               268   \n",
       "3       2785     155     18                               242   \n",
       "4       2595      45      2                               153   \n",
       "\n",
       "   Vertical_Distance_To_Hydrology  Horizontal_Distance_To_Roadways  \\\n",
       "0                               0                              510   \n",
       "1                              -6                              390   \n",
       "2                              65                             3180   \n",
       "3                             118                             3090   \n",
       "4                              -1                              391   \n",
       "\n",
       "   Hillshade_9am  Hillshade_Noon  Hillshade_3pm  \\\n",
       "0            221             232            148   \n",
       "1            220             235            151   \n",
       "2            234             238            135   \n",
       "3            238             238            122   \n",
       "4            220             234            150   \n",
       "\n",
       "   Horizontal_Distance_To_Fire_Points  ...    Soil_Type_32  Soil_Type_33  \\\n",
       "0                                6279  ...               0             0   \n",
       "1                                6225  ...               0             0   \n",
       "2                                6121  ...               0             0   \n",
       "3                                6211  ...               0             0   \n",
       "4                                6172  ...               0             0   \n",
       "\n",
       "   Soil_Type_34  Soil_Type_35  Soil_Type_36  Soil_Type_37  Soil_Type_38  \\\n",
       "0             0             0             0             0             0   \n",
       "1             0             0             0             0             0   \n",
       "2             0             0             0             0             0   \n",
       "3             0             0             0             0             0   \n",
       "4             0             0             0             0             0   \n",
       "\n",
       "   Soil_Type_39  Soil_Type_40  Cover  \n",
       "0             0             0      5  \n",
       "1             0             0      5  \n",
       "2             0             0      2  \n",
       "3             0             0      2  \n",
       "4             0             0      5  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load in the data.\n",
    "\n",
    "raw_data = pd.read_csv('covtype.data', names=col_names)\n",
    "raw_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploring the Data\n",
    "\n",
    "Let's take a look at what we've got here. We already know what the columns mean from the documentation. The numeric features are self-explanatory with their column names. The 4 'Wild_Area' columns correspond to the following:\n",
    "\n",
    "1. Rawah Wilderness Area\n",
    "2. Neota Wilderness Area\n",
    "3. Comanche Peak Wilderness Area\n",
    "4. Cache la Poudre Wilderness Area\n",
    "\n",
    "Similarly, the 40 'Soil_Type' features correspond to 40 specific soil types. Here are the first five:\n",
    "\n",
    "1. Cathedral family - Rock outcrop complex, extremely stony.\n",
    "2. Vanet - Ratake families complex, very stony.\n",
    "3. Haploborolis - Rock outcrop complex, rubbly.\n",
    "4. Ratake family - Rock outcrop complex, rubbly.\n",
    "5. Vanet family - Rock outcrop complex complex, rubbly.\n",
    "\n",
    "Luckily we don't need to know anything about soil to create our models. Each record should correspond to a single 'Wild_Area' and a single 'Soil_Type'. \n",
    "\n",
    "Our seven outcome 'Cover' types are (by corresponding integer):\n",
    "\n",
    "1. Spruce/Fir\n",
    "2. Lodgepole Pine\n",
    "3. Ponderosa Pine\n",
    "4. Cottonwood/Willow\n",
    "5. Aspen\n",
    "6. Douglas-fir\n",
    "7. Krummholz\n",
    "\n",
    "Let's dig a little bit deeper into the numeric features in particular."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Do we have any missing data? \n",
    "\n",
    "raw_data.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data is nice and clean! Many thanks to the creators of this data set.\n",
    "\n",
    "### Distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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HE0u1rVclPS6l1Wh4dtYE6pvbePKdL3tXGlsdrN5+ghtX5PLj1/bwxx0l+PvouX9KPN+f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pISTCYT6enpN9RjUocJ0fjcTv2lUhSl5jqJK1A2surXX39FURTmzJlDWloahYWFREdH20djKYrC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\ndhRFADAnbAAAc8IGADAnbAAAcwFj/WEAGFkRJgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a1f8a0630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's look at the distributions of our numeric variables.\n",
    "\n",
    "g = plt.figure(figsize=(10,18))\n",
    "i = 1\n",
    "for col in raw_data.columns[:10]:\n",
    "    plt.subplot(5, 2, i)\n",
    "    sns.distplot(raw_data[col])\n",
    "    i+=1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A few interesting things here: It appears that Slope, and to a lesser extent, Aspect are clumped around common values. Most likely they rounded Slope to the nearest 5 degrees and Aspect to the nearest 45 degrees during measurement. Other than that, this data is quite clean. Most of the data points are between 2500m and 3500m elevation, which is reasonable for Colorado wilderness. \n",
    "\n",
    "There aren't any outliers to speak of, and the features are all fairly smooth. Aspect has an interesting quality, which is that it actually wraps around -- 360 degrees is the same as 0 degrees. We could potentially deal with this by transforming the data by taking the cosine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wild_Area_1    260796\n",
      "Wild_Area_2     29884\n",
      "Wild_Area_3    253364\n",
      "Wild_Area_4     36968\n",
      "dtype: int64\n",
      "Soil_Type_01      3031\n",
      "Soil_Type_02      7525\n",
      "Soil_Type_03      4823\n",
      "Soil_Type_04     12396\n",
      "Soil_Type_05      1597\n",
      "Soil_Type_06      6575\n",
      "Soil_Type_07       105\n",
      "Soil_Type_08       179\n",
      "Soil_Type_09      1147\n",
      "Soil_Type_10     32634\n",
      "Soil_Type_11     12410\n",
      "Soil_Type_12     29971\n",
      "Soil_Type_13     17431\n",
      "Soil_Type_14       599\n",
      "Soil_Type_15         3\n",
      "Soil_Type_16      2845\n",
      "Soil_Type_17      3422\n",
      "Soil_Type_18      1899\n",
      "Soil_Type_19      4021\n",
      "Soil_Type_20      9259\n",
      "Soil_Type_21       838\n",
      "Soil_Type_22     33373\n",
      "Soil_Type_23     57752\n",
      "Soil_Type_24     21278\n",
      "Soil_Type_25       474\n",
      "Soil_Type_26      2589\n",
      "Soil_Type_27      1086\n",
      "Soil_Type_28       946\n",
      "Soil_Type_29    115247\n",
      "Soil_Type_30     30170\n",
      "Soil_Type_31     25666\n",
      "Soil_Type_32     52519\n",
      "Soil_Type_33     45154\n",
      "Soil_Type_34      1611\n",
      "Soil_Type_35      1891\n",
      "Soil_Type_36       119\n",
      "Soil_Type_37       298\n",
      "Soil_Type_38     15573\n",
      "Soil_Type_39     13806\n",
      "Soil_Type_40      8750\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Value counts for each of the binary features.\n",
    "\n",
    "print(raw_data.loc[:, 'Wild_Area_1':'Wild_Area_4'].sum())\n",
    "print(raw_data.loc[:, 'Soil_Type_01':'Soil_Type_40'].sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most of our data come from the second and fourth Wilderness Area. There are several rare soil types (< 500 examples), and a few with more than 50,000 examples. The rarity of certain soil samples means that we will miss some entirely in either our training set, cross validation, or our test set. That shouldn't be too big a deal, although it may mean with misclassify a handful of examples if those soil types are highly predictive.\n",
    "\n",
    "And finally, let's look at our outcome variable, 'Cover'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KogQr7ktDN5tEapWEfzhpZHATkUbCBbfb54fPryCte6vEIE5wR2yVGA3cZIqINBEuuJ3u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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x108e8ed30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    211840\n",
      "2    283301\n",
      "3     35754\n",
      "4      2747\n",
      "5      9493\n",
      "6     17367\n",
      "7     20510\n",
      "Name: Cover, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "sns.distplot(raw_data.Cover)\n",
    "plt.show()\n",
    "print(raw_data.Cover.value_counts().sort_index())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're dealing with a class imbalance here, so we'll want to resample the rare classes when we train our model. This also means that accuracy alone will be insufficient for judging the quality of our model. We'll need to look at the confusion matrix, precision, and recall as well. And we'll have to be sure to get some of each cover type in our test set!\n",
    "\n",
    "### Correlations\n",
    "\n",
    "Finally, let's look at the correlations between some of the numeric data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NZdasWQAUFBTQokULoKjm95+5ffu2/sPw8vIC4Nq1a8ybNw9zc3Nu3LiBm5tb\nhfp59epVGjVqVOzYokWLWLx4Mbdv39avVPwjW1tbgoKCsLCwIC0tDVdXVwBatmyJSqVCpVLpa67/\n+uuvdOzYEYA+ffoAEBYWxqpVq1izZg06ne6JeytlkZaWRq9evYCi+zWOjo5cvnz5qQm9fv36REdH\n65/v37+fhIQElEol/fv3Z8eOHaSkpDB06FB+++03WrZsqV95+cILL+ive/SzenSOubk5AJ06deL4\n8eO0atVK3z9XV1f9e3vxxRdJSUkhNTVV/4vcqVMnfdxhw4axZcsW6tevz5tvvlnuz0MIIaqSroSp\n9L+aZ5bQn3/+ebKzs/n8888ZMmQIUJQMwsPDcXBwIDk5mVu3bgEUu3+gUCh4vGR7w4YNuXTpEi1a\ntCAqKgpHR0dCQ0PZs2cPlpaWBAUFPXFNWeTn5/P555/zr3/9q9ixnTt36qegPT09eeONN1AoFGi1\nWh48eMDSpUv5/vvvAXj77bf1bSsUT/4CODs7c+bMGbp37862bdu4d+8eTk5OjBkzBjc3N1JTUyu0\n2M7JyYnjx4/Tu3dvsrKySElJ4R//+Ee54wAMGTKEadOmkZ2dzdSpU7lz5w7nz5/X3+M5d+6c/txH\n77FZs2ZcuHBBf1slKSmp2B9mTk5ObNiwgcLCQpRKJcePH2fYsGFcu3ZNf0/p1KlT+vNff/11hg8f\nTt26dVmxYkWF3ocQQlQV2SnuMUOGDGHRokXs378fgJkzZxIUFKS/9zxv3jz9ArhHOnToQEREBE2a\nNNEfmzVrFsHBwSiVSuzs7PDz82PgwIGMHDkSMzMzGjRo8EScp0lJSUGtVqNQKNBoNAwYMIDu3btz\n5coVAExMTLC2tmbYsGHUqVMHd3d3HBwcaNeuHQsXLsTZ2Rk3Nze8vb1RqVRYWVlx8+bNYv39oylT\nphAaGsrKlSupU6cOixYtolevXsycOZO8vDxyc3PLvNr8j4YPH86MGTMYMWIEubm5vP/++9jY2JQ7\nDoCDgwMmJiZ07NgRIyMj7OzsGDNmDMOGDcPW1rbY1y8esbW1ZcKECYwePRqFQoGjoyPe3t76xZBt\n2rTh1VdfxcfHB61WS5cuXXjllVfo0KEDU6ZMYfv27TRs2FA/gjczM8PV1ZXs7GysrKwq9D6EEKKq\n6LR//RG6QleRoayo9caNG8dHH31E06ZNDdrO/v37sbOzo127diQmJrJu3To+++wzoGitxIABA+jc\nuXOJ1x6B2LaxAAAgAElEQVR1GGzQvhVoDfslEEOXT7Uebvh1B4Yun/pV+9pQPtWw8aV8atm4X/+q\nUtf/1qlPmc9tdnxvpdqqqL/FTnHLly/n2LFjTxwPCwszeMKqqIkTJ3Lv3r1ixywtLfXbBZbFpk2b\n+O677544PnnyZNq3b1/iNdnZ2ajVatzd3Z/JZ/OPf/yDGTNmoFKp0Gq1hIaGAjB69Gjs7e2fmsyF\nEOJZkhG6EAYkI/Q/JyP00skIvXQyQi/ya4e+ZT7X8dSeSrVVUX+LEboQQghRGTVhhC4JXQghhCiF\nfG1NCAN63v2OQeMfS2xU+kmVsPKKQ+knVULfOdcNGh/gyjzDTokPPV2+OgXl9X3baQaND9C8/r3S\nT6qEe/crVsijrNxcDf97tO6M4dfruFfyevnamhBCCFELaGWELoQQQtR82sK/frVxSehCCCFEKWrC\n98EkoQshhBClkFXuQgghRC0g99CFEEKIWqBWfm3t2LFjfPnll0RGRuqPRURE4OTkxODBf75z188/\n/8zevXuZOHFi+Xv6mPPnz3P//v2nbg1aUj8ff/2DDz7AxcUFnU6HRqNh9OjReHp6ltrPpKQk6tat\nqy8N+qz4+vqi1WpJS0ujfv361KtXj+7duzNhwoQyx0hPT2fw4MG0adMGgNzcXOrWrcvHH39cqSIo\n4eHhtGrVSkqdCiFqJbmH/pjWrVvTunXrKom1e/duGjRoUKm9vrt27apP+I/2MHd0dCy1n5s3b8bT\n0/OZJ/T169cDMHXqVDw9PenRo0eF4rRs2bJYLfTw8HDi4+Px8/Orim4KIUStU2jgraCrQpUm9AUL\nFpCcnAxA//798fX1ZerUqWRmZpKZmcnYsWPZsWMHgYGBBAcHA0WJNC0tjSNHjrBnzx7Wr1+PiYkJ\nLVq0YPbs2Wzfvp0DBw6Qm5vLb7/9xjvvvIO7uztbtmzB2NiYtm3bkpGRwcaNG/VlWJcvX17uvltY\nWODt7c3OnTu5f/++fnQ/bdo00tPTyc3NZfTo0bi4uPDDDz9w9uxZXFxc2LdvH7t37+bhw4fY2Niw\nfPlyvvnmmyf6PHjwYE6dOkVYWBharRZ7e3siIiJIT09n7ty5ANSrV4+wsDDq1q1brr5nZmYyefJk\ncnJyKCwsJDAwkC5dupTpWq1Wy/Xr12nZsiUAq1evZufOnahUKl566SUCAwPJyMhg1qxZ5Ofnc+vW\nLQIDA+nduzc7duwgKiqK+vXrk5eXR6tWrfD39+f999+ndevW9O3bl6lTp9KnTx98fX2JiIhgx44d\n7N27l5ycHBo0aMCyZcuYNGkSQ4cOxcPDgwsXLhAZGcmkSZOYPn06KpUKnU7HkiVLsLe3L98PVQgh\nqkitHaEfPXoUtVqtf3758mXGjRvHlStXiI2NRaPRMGLECLp27QoUjYT9/Pz0Fc+aNm1KdHQ0+fn5\n+Pv788knn5Cbm8uyZcvYsmULlpaWhIWFERMTg7m5OVlZWaxdu5ZLly7h7+/P4MGDGTRoEA0aNKB9\n+/YcPnyYqKgozMzMCA0N5eDBgxX6x9/W1pazZ8/qn2dlZZGUlERsbCwAhw4dol27dnh4eODp6Umj\nRo3IzMxk3bp1KJVKxo4dy5kzZ/TXPt7n0NBQlixZgrOzM3FxcaSmpjJr1izCwsJwcXEhLi6ONWvW\nEBAQUK5+r1ixgl69ejFy5EiuXbuGWq0mISHhqedfuHABtVpNZmYm+fn5DBgwgIEDB3Lu3Dn27t1L\nTEwMRkZG/Pvf/yYxMRGlUsk777xDp06dSEpKYtWqVbz88sssXLiQr7/+GisrK8aOHQvAq6++SmJi\nImZmZpiZmXHkyBE6deqEVqulfv36PHjwgHXr1qFQKPDz8+PcuXMMGzaM+Ph4PDw8+Oqrrxg6dCgH\nDx6kY8eOfPjhhyQlJXH//n1J6EKIalNrF8X9caoaiu6h5+bm0qlTJxQKBcbGxnTo0IHU1FQAHB0d\nn4ih0WgICAhg4MCB9OzZk9OnT+Pi4oKlpSUAnTt35uDBg3To0EE/td24cWPy8/OfiGVra0tQUBAW\nFhakpaXh6upakbdFRkYGjRr933aflpaWBAcHExISQlZWFgMHDix2vlKpxNjYmMDAQMzNzbl+/Toa\njQagxD7fvn0bZ2dnALy8vAD0SR2goKCAFi1alLvfaWlpDB06VN+eqakpd+/excbGpsTzH025P3z4\nkPHjx9OwYUOMjIz0n51KVfRr8eKLL5KSkoK7uzurVq0iNjYWrVaLRqPh9u3b2NraYm1tDUDHjh0B\n6N27N++//z7m5ub4+/uzdu1avv/+e/r06YNSqUSpVOo/r1u3blFQUED37t0JCwvjzp07HD16lKCg\nIAoKCli9ejVjx47FysqKwEDDVvUSQog/UxMWxVXZTYE6derop9sLCgo4ceIEzZs3B0ChKP5B6HQ6\npk+fTseOHXnrrbcAaNKkCampqeTk5ADw448/6v8QePz6R8e0Wi0PHjxg6dKlREZGMnfuXExNTalI\nRdisrCzi4uLo16+f/tjNmzc5e/YsK1asICoqikWLFumn9XU6Hb/88gsJCQl8/PHHhISEoNVq9W2X\n1OeGDRty6dIlAKKiotizZw+Ojo6Eh4cTHR3N5MmT6dWrV7n77uTkxPHjxwG4du0aOTk5ZVrgZmZm\nxuLFi/nkk0+4cOECTk5OnDx5ksLCQnQ6HcePH6dFixZERkYyZMgQFi5cSJcuXdDpdNjZ2XH37l3u\n3r0LwE8//QRA/fr1UalU7N69Gw8PD+zs7Ni0aRN9+/bl3LlzJCYm8vHHHzNjxgwKCwv1n1X//v2Z\nO3cuPXv2xMjIiD179vDSSy+xfv16+vTpw9q1a8v9uQghRFXR6hRlflSXKruHbm5uTpMmTfD29qag\noIB+/frRtm3J9Zh37tzJ7t27uXHjBgcOHADgo48+4t1332X06NEolUqaNWvGpEmT+Pbbb0uM0a5d\nOxYuXIizszNubm54e3ujUqmwsrLi5s2bNGnSpNQ+P7p1oFQqKSws5N1338XJyYlbt24BYGdnx61b\nt/Dx8UGpVDJmzBhUKhUdOnQgIiKCJUuWYGZmho+Pj/78mzdvPrW9WbNmERwcjFKpxM7ODj8/Pxo3\nbkxQUJD+D4V58+aV2u/HTZgwgeDgYHbs2EFubi5z587FyMioTNc2bNiQSZMmERoayhdffMGrr76K\nj48PWq2WLl268Morr/DgwQPCwsKoV68ejRo14s6dOxgbGzN9+nTGjBmDtbV1sfZ69+7Nt99+S926\ndfHw8GDz5s384x//wMbGBmNjY4YPH65v+9HnNXjwYP19eSj6+QYHB2NsbIxOp9OvuRBCiOpQA26h\no9BVZDgrRBW7du0a06dP57PPPivzNXe9ehmuQxi+2tphs7L90VVRfXPzDBof4IrS1KDxpdpa6Qxd\nbc3J1bBVDeHZVFv78LcNlbr+UKOhZT7X/fpXlWqromr9xjIzZ87U38v/o9WrV1OnTp1q6NGfy8/P\n1y8w+yNHR0dmz55d5jhLly4lKSnpiePh4eE4OBi2bGd5fffdd/znP/9hzhzD/uMthBAVVQOqp/49\nEnpNYmJiUuw74hX13nvvVUFvno3XX3+d119/vbq7IYQQT6Xjr78ortYndCGEEKKytDXg5rQkdCGE\nEKIUWhmhCyGEEDVfoSR0IQwn9Ug9g8b/kF8NGr8bhl3Ze0Nh2BXoAJeNDfuPnKFXofc6O9+g8QF2\nt51u0Phv3k00aPzjP71o0PgAzQr++vPZNeEe+l9/t3khhBCimmnL8XjiWq2W0NBQvL29UavVpKen\nF3t93759DBkyBG9vb/1W4xUhCV0IIYQoRWUSekJCAvn5+cTExPDhhx+yYMEC/WsFBQXMnz+fzz77\njOjoaGJiYrh9+3aF+igJXQghhCiFDkWZH49LTk7Gw8MDAFdXV/1W2VBUz6NZs2ZYW1tjYmLCiy++\nWOIeImUh99CFEEKIUmgrcQs9KytLX3gMwMjICI1Gg0qlIisrq1jJbAsLC7KysirUjiR0IYQQohSV\nWeVuaWlJdna2/rlWq9VXtXz8tezs7GIJvjxkyl0IIYQoRWXuobu5uZGYWPRthJMnT9KyZUv9a87O\nzqSnp5OZmUl+fj7Hjx/Xl6MuL0noNcSxY8cICAgodiwiIoJ169axfPlyANzd3QFQq9Ul7l8PEB8f\nT0RERIX68Ch+eWRmZvLOO+8wfPhwJkyYwO+//16htoUQojppFYoyPx7Xt29fTExM8PHxYf78+Uyb\nNo3t27cTExODsbExU6dOZezYsfj4+DBkyBDs7e0r1EeZcq/hrKys8PPzq+5uPNWqVat48cUX8ff3\n5/DhwyxZsqRCJWKFEKI6Veab8kql8oniWs7Ozvr/7t27N717965EC0UkodcCAQEBREZGPnE8OTmZ\n8PBwVCoVZmZmfPLJJwCcOnWKMWPGcOfOHYYPH463tzc7d+5k48aN+rrsy5cvx9rampCQEFJSUmja\ntCn5+flAUanTkJAQ8vLyMDU1Zc6cOTRu3LjEvqWkpOhnFtzc3PS/1CW1d/HiRaKiojA2Nub69ev4\n+Phw9OhRfvnlF0aPHs2IESMM8fEJIUSppNqaqFJHjx5FrVbrn1++fPlPq6olJCTw+uuv4+vry759\n+7h//z4AKpWKtWvXcvXqVcaPH4+3tzeXLl0iKioKMzMzQkNDOXjwICYmJuTl5REbG0tGRga7du0C\nikqwqtVqevbsyZEjR4iIiGDx4sUl9qF169bs27ePNm3asG/fPnJzcwFKbM/e3p7r16+zdetWzp49\ny/vvv8+ePXu4ceMGEydOlIQuhKg2lVnl/qxIQq9BunbtWmwkXtq9cH9/fz799FN8fX2xt7enffv2\nALRp0waFQoGdnZ0+wdra2hIUFISFhQVpaWm4urqSkZGhv8bBwUE/Cr9w4QKrVq1izZo16HQ6/WrN\nkowfP5558+YxcuRIevbsSaNGjZ7aHsBzzz2HsbExdevWpVmzZpiYmGBtbU1eXl4FPzUhhKg82ctd\nVKtt27YxaNAggoKCWLVqFbGxsTg4OKB4bNHGgwcPWLp0Kd9//z0Ab7/9NjqdDhcXF7799lt8fX25\nceMGN27cAMDJyYkxY8bg5uZGamrqn26CcPz4cby8vHBzc2PXrl24ubk9tT3gib4JIcRfgYzQRbVq\n3749M2bMwMzMTL8oo6Tka2lpiZubG97e3qhUKqysrLh58yaDBw/m0KFDeHl54eDggI2NDQBBQUHM\nnDmTvLw8cnNzmT796cUnHB0dCQoKAqBhw4aEhYVhYWFRYntNmjQxzAchhBCVVBPuoSt0j4ZGQtQw\nx5u8ZdD4vgXXDBq/m5lhq631yzN8tbU0E8MOWzrmFhg0vlRbK93xxoavtnYxv2IbqZSH17WNlbr+\nv/8YVeZz3766oVJtVZSM0EWVmDhxIvfu3St2zNLSkpUrV1ZTj4QQourIlLv423i0uY0QQtRGmuru\nQBlIQhdCCCFKoZMRuhBCCFHz1YRFcZLQhRBCiFJIQhfCgGzqPTRo/ETHOgaN/3ryLYPGH65tZND4\nAFZaY4PGb17/XuknVYKhV6ADvHbWsLUL7m9caND4P4YZvqBSpvFffz67JnwdTBK6EEIIUQpZ5S6E\nEELUArLKXQghhKgFZMpdCCGEqAVkyl0IIYSoBWSVuxBCCFEL1IQpd2V1d0A86dixYwQEBBQ7FhER\nwbp16/RbrLq7uwOgVqtJTU0tMU58fHypNdOf5lH88oiPj6d3795kZWXpjwUEBHDs2LEK9UEIIf4q\nNOjK/KguktBrECsrKyZOnFjd3fhTDx8+JCwsrLq7IYQQVUpXjkd1kSn3GiYgIIDIyMgnjicnJxMe\nHo5KpcLMzIxPPvkEgFOnTjFmzBju3LnD8OHD8fb2ZufOnWzcuBGNRoNCoWD58uVYW1sTEhJCSkoK\nTZs2JT8/H4Br164REhJCXl4epqamzJkzh8aNGz+1f2+99RYnTpxg//79vPLKK8VeW7BgAcnJyQD0\n798fX19frly5QnBwMIWFhSgUCmbMmEGrVq147bXXcHNz49dff8XW1pZly5ZhZGRUVR+jEEKUi9xD\nFxV29OhR1Gq1/vnly5d57733nnp+QkICr7/+Or6+vuzbt4/79+8DoFKpWLt2LVevXmX8+PF4e3tz\n6dIloqKiMDMzIzQ0lIMHD2JiYkJeXh6xsbFkZGSwa9cuAMLDw1Gr1fTs2ZMjR44QERHB4sWLn9oP\nIyMjFixYwDvvvIOrq6v++P79+7ly5QqxsbFoNBpGjBhB165dWbFiBaNHj+bVV1/l559/Jjg4mPj4\neC5fvsz69etp3LgxPj4+nDlzplg8IYR4lmSVu6iwrl27FhuJl3Yv3N/fn08//RRfX1/s7e1p3749\nAG3atEGhUGBnZ0dubi4Atra2BAUFYWFhQVpaGq6urmRkZOivcXBw0I/CL1y4wKpVq1izZg06nQ6V\nqvRfmRYtWjB69GhmzZqFQlH0/4LU1FQ6deqEQqHA2NiYDh06kJqaSmpqKp07dwagdevWXL9+HQAb\nGxt9Hxo3bkxeXl6ZPzshhKhq2hqwLE7uodcS27ZtY9CgQURHR/Pcc88RGxsLoE+ojzx48IClS5cS\nGRnJ3LlzMTU1RafT4eLiwsmTJwG4ceMGN27cAMDJyYlJkyYRHR3NrFmz6NevX5n6M2rUKO7evcvR\no0cBcHZ21k+3FxQUcOLECZo3b46zszPHjx8H4Oeff6ZBgwYl9lsIIaqT3EMXz0z79u2ZMWMGZmZm\nKJVKZs+eTVJS0hPnWVpa4ubmhre3NyqVCisrK27evMngwYM5dOgQXl5eODg4YGNjA0BQUBAzZ84k\nLy+P3Nxcpk8vWzELhULB/PnzGTBgAACvvPIKP/74I97e3hQUFNCvXz/atm3LlClTCAkJ4bPPPkOj\n0TBvnmELWQghREVU5+r1slLodLq/fi+FKEFqu38aNH49x1yDxn892aDhmV9o+GprF00MW22tt+Vt\ng8a/eMfGoPHB8NXWCmpBtbWLxqYGb+OdKxsqdf2UFsPLfO7CS19Uqq2KkhG6KLeJEydy717xspaW\nlpasXLmymnokhBCGJavcRa30aHMbIYT4u6gJi+IkoQshhBClqOp0npuby+TJk/n999+xsLAgPDyc\n+vXrFztn48aNxMfHo1AoGDNmDJ6enn8aU1a5CyGEEKXQluNRFl988QUtW7Zk06ZNvPXWW/znP/8p\n9vqdO3f44osv+PLLL1m3bh3h4eGUtuRNRuiixrp8y8qg8R0+7GDQ+INOZxg0vqIw36DxAZzzCwwa\n/959M4PGf/NuokHjA9w38KI145FTDBr/WniIQeMDWGr/+tPZhVU8Rk9OTmbcuHEA9OjR44mEXr9+\nfbZu3YpKpeLq1auYmpqW+nVeSehCCCFEKSpzDz0uLo7169cXO2Zra0vdunUBsLCw4MGDB09cp1Kp\n2LBhA8uWLSu2c+jTyJS7EEIIUYrKbCzj5eXFN998U+xRt25dsrOzAcjOzsbKquQZx1GjRvHDDz+Q\nlJSk36jraSShCyGEEKXQoivzoyzc3Nw4cOAAAImJibz44ovFXk9LS2PixInodDqMjY0xMTFBqfzz\nlC1T7kIIIUQpqvp76MOHDycoKIjhw4djbGysL3r13//+l2bNmtGnTx9atWqFt7c3CoUCDw8PunTp\n8qcxJaELIYQQpajqRXFmZmYsXbr0ieNvv/22/r8nTpzIxIkTyxxTEroQQghRCp1sLCOEEELUfDVh\n61dZFFdDHDt2jICAgGLHIiIiWLdunX4rVnd3dwDUajWpqaklxomPjy+1tvrTPIpfHjk5OUyYMIGR\nI0fi5+enL8sqhBA1iVanK/OjukhCr+GsrKzKdY/lWYuNjaVt27Zs3LiRgQMHsnr16urukhBClJvU\nQxfPREBAAJGRkU8cT05OJjw8HJVKhZmZGZ988gkAp06dYsyYMdy5c4fhw4fj7e3Nzp072bhxIxqN\nBoVCwfLly7G2tiYkJISUlBSaNm1Kfn7RzmPXrl0jJCSEvLw8TE1NmTNnDo0bNy6xb35+fhQWFgKQ\nkZGh/66lp6cnnTp14uLFi1hbW7NkyRJ27tzJ/v37yc3N5datW4wePZq9e/dy8eJFpkyZwquvvmqI\nj08IIUolxVlElTp69Gix3YIuX77Me++999TzExISeP311/H19WXfvn3cv38fKNp9aO3atVy9epXx\n48fj7e3NpUuXiIqKwszMjNDQUA4ePIiJiQl5eXnExsaSkZHBrl27AAgPD0etVtOzZ0+OHDlCRESE\n/isXJTEyMmL06NFcuHCB//73v0BRYYIBAwbQuXNnFi5cSExMDNbW1mRnZ/PZZ5/x7bffsm7dOmJj\nYzl27Biff/65JHQhRLWp6lXuhiAJvQbp2rVrsZF4affC/f39+fTTT/H19cXe3p727dsD0KZNGxQK\nBXZ2duTm5gJF2xAGBQVhYWFBWloarq6uZGRk6K9xcHDQj8IvXLjAqlWrWLNmDTqdDpWq9F+jzz//\nnNTUVP71r3+RkJCASqWic+fOQNEGC4mJibi6utK6dWsA6tati7OzMwqFAmtra/Ly8sr5aQkhRNWp\nCSN0uYdei23bto1BgwYRHR3Nc889R2xsLMATG/w/ePCApUuXEhkZydy5czE1NUWn0+Hi4sLJkycB\nuHHjhn5Bm5OTE5MmTSI6OppZs2bRr1+/p/Zh1apVbN26FSjar9jIyAgAjUbDL7/8AhTdGnBxcSmx\nb0II8VegK8f/qouM0Gux9u3bM2PGDMzMzFAqlcyePZukpKQnzrO0tMTNzQ1vb29UKhVWVlbcvHmT\nwYMHc+jQIby8vHBwcMDGxgaAoKAgZs6cSV5eHrm5uUyfPv2pfRgyZAhBQUFs3ryZwsJCwsLC9K+t\nXr2ajIwMHBwcCAgI4Jtvvqn6D0EIIapATfjamkJXWoFVIQygd+/efPfdd5iamlY4xvf2XlXYoye9\ntMDRoPE/nmnY8qkv5Rq+fGohhp1Rqacy7HvodutHg8YHuL9ogEHjG7p86lftDV8+9VkYnrGxUtcP\nalb2n+OW37ZXqq2KkhG6qBITJ07k3r17xY5ZWlqycuXKauqREEJUnZpwD10SuqgSjza3Kat9+/YZ\nqCdCCFH1ZJW7EEIIUQvICF0IIYSoBWrCcjNJ6KLGsjXPNWj89aGGXbSmVf71/4EozTWVsUHju7le\nN2j84z+9aND4AD+G/W7Q+NfCDbtobejpOQaND7C2Y6jB26ismrDKXRK6EEIIUQopnyqEEELUAoW6\nv/4YXRK6EEIIUQpZFCeEEELUAjLlLoQQQtQCWlnlLoQQQtR8f/10XoZqa8eOHSMgIKDYsYiICOLj\n40sN/vPPP5d7B7GnOX/+fImFRR4pqZ+Pv96tWzfUajWjRo3Cx8eHHTt2lKmfSUlJ+spgz5Kvry9q\ntRp3d3cGDBiAWq0u91aqGo2Gdu3aoVar9Y85c+Zw48YN5syp3NdRDh8+TPfu3fVxhw0bxsaNT98v\nuSxt/tn1QghRXbToyvyoLgYdobdu3Vpf37qydu/eTYMGDfQ1tCvij/XEs7OzUavVODo6ltrPzZs3\n4+npSatWrSrcdkWsX78egKlTp+Lp6UmPHj0qFKd+/fpER0c/cTwkpPLfX+3evbu+Lnt+fj6vvfYa\nb775JpaWlk+ca29v/6dtajQaVq1axciRIyvdLyGEqEq1fpX7ggULSE5OBqB///74+voydepUMjMz\nyczMZOzYsezYsYPAwECCg4OBokSalpbGkSNH2LNnD+vXr8fExIQWLVowe/Zstm/fzoEDB8jNzeW3\n337jnXfewd3dnS1btmBsbEzbtm3JyMhg48aNaDQaFApFhWYBLCws8Pb2ZufOndy/f58vv/ySyMhI\npk2bRnp6Orm5uYwePRoXFxd++OEHzp49i4uLC/v27WP37t08fPgQGxsbli9fzjfffPNEnwcPHsyp\nU6cICwtDq9Vib29PREQE6enpzJ07F4B69eoRFhZG3bp1y9X3zMxMJk+eTE5ODoWFhQQGBtKlS5dy\nxUhPT2fq1Kl88cUXvPHGG7Ro0YI6deoQGhrK9OnTuXfvHgqFgtDQUH2t8tJkZWVhZGSESqXizJkz\nzJs3D5VKhampKXPnziU/P1/f5oABA+jUqRMXLlzAyMiI//znP6xbt447d+4wZ84cRowYwfTp01Gp\nVOh0OpYsWYK9vX253qMQQlSVWrPK/ejRo6jVav3zy5cvM27cOK5cuUJsbCwajYYRI0bQtWtXoGgk\n7Ofnx7FjxwBo2rQp0dHR5Ofn4+/vzyeffEJubi7Lli1jy5YtWFpaEhYWRkxMDObm5mRlZbF27Vou\nXbqEv78/gwcPZtCgQTRo0ID27dtz+PBhoqKiMDMzIzQ0lIMHD1boH3tbW1vOnj2rf56VlUVSUhKx\nsbEAHDp0iHbt2uHh4YGnpyeNGjUiMzOTdevWoVQqGTt2LGfOnNFf+3ifQ0NDWbJkCc7OzsTFxZGa\nmsqsWbMICwvDxcWFuLg41qxZ86e3CkqyYsUKevXqxciRI7l27RpqtZqEhISnnn/nzp1iP7/g4GDM\nzc31zx88eMB7773H888/z4IFC+jRowfDhg0jNTWVjz76iA0bNjw19uHDh1Gr1SgUCoyNjZk5cyZ1\n6tQhJCSE8PBwnn/+eXbt2sXChQv54IMP9Nfdu3ePQYMG0b59ez744AMOHjyIv78/sbGxhISEsH79\nejp27MiHH35IUlIS9+/fl4QuhKg2tWaV+x+nqqHoHnpubi6dOnXS/0PeoUMHUlNTAXB0fLKOtEaj\nISAggIEDB9KzZ09Onz6Ni4uLfmq2c+fOHDx4kA4dOuinths3bkx+/pP1kG1tbQkKCsLCwoK0tDRc\nXV3L/86BjIwMGjVqpH9uaWlJcHAwISEhZGVlMXDgwGLnK5VKjI2NCQwMxNzcnOvXr6PRaABK7PPt\n2w+P1o8AACAASURBVLdxdnYGwMurqHb3o6QOUFBQQIsWLcrd77S0NIYOHapvz9TUlLt372JjY1Pi\n+SVNuaenpxd7/uhnduHCBY4fP8727UX1fB8vifq4P065/9Ht27d5/vnngaKfbUmzKI9uc/z/9u48\nrsa0jQP477RSiYgsoaIFIya9aCzZhkpkiTbZ16gZ2SqNFJWmhMYyNBkkSqMxBsPQ2DJGRMaWpjR2\nhUraT/W8f/Q5Z4ooup+Ozrm+n4/PezrHXM/zqLfrea77uu+7Xbt2KCkpqfaZnZ0dwsPDMWvWLKir\nq8Pd3f2950EIIXyS6rXcmzRpgkuXLmH69OkQCoW4du0axo8fDwAQCATV/i7HcVi5ciU+//xzjBs3\nDgCgra2N9PR0FBYWQkVFBYmJieKk8uZ/L3qvoqICr1+/RlhYGM6cOQMAmDFjxkf9Q+fn5yM2Nhab\nNm3C8+fPAQBZWVm4desWtmzZgpKSEpibm8PGxgYCgQAcxyElJQWnTp1CbGwsioqKMGHCBPGxazrn\nNm3a4N9//4WOjg527NgBXV1d6OrqIigoCO3bt0dSUpL42B9CT08PV65cgaGhIZ4+fYrCwkKoq6t/\ncJyq5OTkxLFNTExgZWWF58+f4+eff/6oeJqamvjnn3+gr6+PxMTEGm9c3vw3k5OTQ0VF5TjVyZMn\n0a9fP7i6uuLQoUOIiIgQD1UQQkhDk5qSe01UVFSgra0NOzs7CIVCWFhYoEePHjX+3ePHj+P3339H\nZmYmzp49CwDw8fGBq6srpk6dCjk5OXTq1AlLly7F0aNHa4zx2Wef4dtvv0WXLl1gYmICOzs7KCgo\nQF1dHVlZWdDW1q71nEVDB3JycigvL4erqyv09PTESbV169Z4/vw57O3tIScnh5kzZ0JBQQG9evVC\nSEgIQkND0bRpU9jb24v/flZW1juP5+vrCy8vL8jJyaF169aYPn062rVrhxUrVojH//39/Ws97zct\nWLAAXl5eOHbsGIqLi7F27VrIy8t/cJyauLi4YOXKldi/fz8KCgrg5ub2UXHWrl0LHx8fAICCggIC\nAgJQXl7+3v9GTk4OnTt3hoeHB+bNmwcvLy8oKiqC4zhxDwYhhEhCY3hCF3CN4SwJqcEN3TG8xr9Q\n1pzX+C953m3NrFjIa3wAeKSgxGv80b0f8hr/4U1+v8cAkFuszGv8p/L8fg+kZbe1+Q/f3QtUF8Zt\nzer8d/9+drFex/pYUrewzOrVq8Vj+VWFh4ejSZMmEjij9ystLcWsWbPeel9XVxd+fn51jhMWFlbj\nPH1Reb8+Vq1ahYyMjLfej4iIgJISv79MCCHkU8B6pbji4mIsW7YML1++hKqqKoKCgtCyZctqf+fs\n2bPYsmULOI5Djx494OPjU+PwrohUJvTGRElJqcY54h/qY0vjdfEhNxaEECKNWHe579+/HwYGBnB1\ndcXRo0exdetWeHt7iz/Pz89HcHAw9uzZg5YtWyI8PBw5OTlvJf2qal0pjhBCCJF1FRxX5z91kZSU\nhEGDBgEABg8ejIsXq5fpr127BgMDAwQFBcHR0RGamprvTeaAFD6hE0IIIazV5wk9NjZWvPKnSKtW\nrcSLiqmqquL169fVPs/JycGlS5dw6NAhqKiowMnJCb17965xWrgIJXRCCCGkFvVZ+nXSpEnitUhE\nFi1ahIKCAgCVK6i+OfW4RYsW6NmzJ1q3bg0AMDU1xZ07dyihE+m0k1PlNf6XpWW8xh+gUsBr/H8E\nH7ak8MfoUl7Ma/xdNzryGr+TkP9JPrmK725iYkGtgt9raIgO9FnXPv0+HdZNcSYmJjh79iyMjY1x\n7tw59OnTp9rnPXr0QGpqKrKzs6Guro7r169j8uTJ741JCZ0QQgipBeumOAcHB6xYsQIODg5QVFTE\n+vXrAQA//vgjOnXqhOHDh2PJkiWYPXs2AMDCwgIGBgbvjUkJnRBCCKkFx3i3taZNmyIsLOyt92fM\nmCF+PXr0aIwePbrOMSmhE0IIIbWQ6qVfCSGEEFnRGBZVpYROCCGE1KI+Xe4NhRI6IYQQUgvWXe58\noIROmPjnn38QHByMoqIiFBYWwtzcHK6uru9dd5gQQhoL1l3ufKCETuotLy8P7u7u+O6776Cjo4Py\n8nJ89dVXiI6OhoODg6RPjxBC6o3G0IlMiI+PR79+/aCjowMAkJeXR1BQEBQVFbFu3TokJSUBAKyt\nreHo6AgrKyv88ssvUFFRQUREBOTl5TFq1Ch88803KCkpgbKyMtasWYPy8nIsWLAALVq0wODBgzFn\nzhwJXiUhRJZRlzuRCVlZWejYsfqKXqqqqjh9+jQePXqEAwcOoKysDI6Ojujfvz9GjhyJ33//HePG\njcORI0ewc+dO+Pr6wtnZGebm5rh48SJCQkKwePFiPH/+HAcPHqRtWgkhElVeQU1xRAa0b98et2/f\nrvbew4cPcevWLZiamkIgEEBRURG9evVCeno6Jk2ahNWrV0NPTw+6urrQ0NBAamoqtm/fjh9++AEc\nx0FBofJHU1tbm5I5IUTiGkPJnbZPJfU2dOhQnD9/Hg8ePAAACIVCrFu3Durq6uJyu1AoxLVr19C5\nc2fo6OiA4zj88MMP4g0L9PT0sHTpUkRGRsLX1xcWFhYAADk5+hElhEheBbg6/5EUekIn9aampoZ1\n69bB29sbHMehoKAAQ4cOhbOzM54+fQo7OzsIhUJYWFigR48eAABbW1uEhYWhf//+AIAVK1Zg9erV\nKCkpQXFxMVauXCnJSyKEkGoawxO6gGsMZ0lIDRbr2PMa/8siXsOjI9+7rRXxv9uaFkp5jf+XUhNe\n4zfIbmvyjXu3tVc8nz/QMLutKWrq1eu/V1N597alb8ovzKjXsT4WPaETQgghtaB56IQQQogUoC53\nQgghRArQEzohhBAiBRpDuxkldEIIIaQWjSGhU5c7IYQQIgVo1Q5CCCFEClBCJ4QQQqQAJXRCCCFE\nClBCJ4QQQqQAJXRC6uH06dPVvj527JiEzuTjZWdnS/oUCCEM0LQ1Qj7C6dOncfXqVRw9ehTXrl0D\nAJSXl+OPP/6AlZUVL8fMzc1FixYtmMd1cXFBmzZtYGtri0GDBkEgYLt29507d9CtWzemMd/0/Plz\n5OXlQUFBAREREXB0dISRkRGvx2SN4zjcuHEDJSUl4vf+97//SfCMPgzf34OIiAjMmjWLWTxpRAmd\nSLU7d+4gJiam2i/JwMDAesc1MjJCbm4ulJWVoaenB47jIBAIYG1tXe/Yb0pMTISfnx/Ky8thYWGB\n9u3bi7edZSE6Ohp3797FwYMHsXnzZgwcOBATJ05Ehw4dmMTftm0bsrKyYGNjgzFjxkBNTY1J3Krc\n3d3h4uKC/fv3Y/jw4QgICMCePXuYxY+OjkZ0dDRKS0vF32vW1RhXV1e8fPkS7dq1AwAIBAKmCf2P\nP/7AwYMHUVr634Y64eHhzOLz/T04e/Yspk+fDnl5eWYxpQ0ldCLVPDw8MGXKFLRt25Zp3Hbt2mH8\n+PEYPHgw7t69iy+++AJRUVHMkmBVmzZtwt69e+Hq6or58+fDwcGBaUIHgA4dOqBr165ITU3FzZs3\ncePGDXTv3h2LFy+ud+ywsDDk5OTg8OHDWLhwIdq2bYtJkybB1NSUwZn/p1+/fti+fTtsbGwQFxfH\nNPaePXuwY8cONG/enGncql68eIHo6Gje4gcFBcHPz4/Xa+Dze5CTk4NBgwZBW1sbAoEAAoGA13+v\nxogSOpFqmpqazJNfVUuXLsXUqVMBAOrq6li2bBm2b9/O9BhycnJo0aIFBAIBlJWVoaqqyjT+kiVL\ncPv2bYwePRqBgYHiJ8QJEyYwSegAkJeXh+zsbOTl5UFPTw+HDx9GbGwsgoKCmMQXCoUICQmBqakp\nrly5AqFQyCSuiKGhIdq1a8fr06Guri4yMzOhpaXFS3x9fX3069ePl9gA/9+D77//nmk8aUQJnUi1\nDh06YMeOHejWrZt4bHjgwIHM4hcVFWHo0KEAgDFjxiA2NpZZbJFOnTph/fr1yMnJwY4dO9C+fXum\n8ceOHYv169e/9f7evXuZxLe3t4ecnBwmT56MBQsWoEmTyj3Op02bxiQ+AAQEBCAhIQF2dnY4efIk\nAgICmMUGgP79+2PEiBHo2LGjuOTOspwMAFevXsXQoUOhoaEh/llNSEhgFn/48OGws7ODnt5/+4Kz\nGH4SefN7wDI2ACgoKCA4OBjZ2dmwsLCAoaEhLxWxxowSOpFqQqEQGRkZyMjIEL/HMqErKiriwoUL\n6NWrF27cuAE5OfYTR3x9fREbGwtTU1OoqKhgzZo1TOPv2rULu3fvFn+toKCAtm3bYv78+VBRUal3\nfH9/f3Tp0uWt96ses75CQkIwadIkKCkp8dLHEBMTg40bN6JZs2bMY4ucOHGCt9gAEBkZidmzZ/N2\nDfv27YO3tzcAwNraGp6enkyT+jfffIMZM2Zg69atMDU1hYeHBw4cOMAsvjSghE6kWmBgIFJTU5GW\nlgZdXV3m3dZr165FUFCQOGn5+fkxjQ8AZWVlKCkpQXl5OQAw70LX0tJCr169YGpqiuTkZJw9exbd\nu3eHl5cXdu3aVe/4CxYsqHbOohuGZcuWMeuCXrx4MX766Sds3LgRQ4YMga2tLTp27MgkNlD5b9Sz\nZ09ebthE7t69Cy8vL2RmZkJTUxMBAQHo3r07s/iampq8zMCIiorCjh07kJ2djVOnToHjOHAch86d\nOzM9TnFxMczMzLBt2zbo6elBWVmZaXxpQAmdSLXIyEgcOXIExsbG2LlzJywtLZlOfencuTOWLVuG\n+/fvw8jIiJfxT3d3d+jp6WHw4MG4evUqPD09ERISwiz+s2fPsG7dOgCV46zHjh2Dvb09fv31Vybx\nzczMMHz4cPENw8GDBzFu3Dj4+flh3759TI6hr68PT09P5OTkYO3atbC0tES/fv3w1VdfwdjYuN7x\nS0tLYWNjA319ffHNSU3DFPWxdu1a+Pv7w8jICHfu3IGvry/Tpq8mTZpg1qxZ6N69u/ga3N3d6x3X\nyckJdnZ2+P7777FgwQIAlX0frG88lZWVcf78eVRUVCA5ORlKSkpM40sDSuhEqh05cgRRUVFQUFCA\nUCiEvb0904S+d+9enDx5Eq9evcL48eNx//59rFq1ill8oHL++dKlSwEAI0aMgKOjI9P4JSUluHjx\nInr37o1r165BKBTi0aNHKCoqYhL/3r178PX1BQB88cUX2L59OwYNGsS0yenChQuIi4tDamoqrK2t\nsWLFCpSXl2PevHk4fPhwvePPmzePwVnWTlSx6NatGxQU2P56FvV68EFBQQEzZ85EfHx8tSmiY8aM\nYXaMNWvWICgoCDk5Odi5cydWr17NLLa0oIROpBrHceJfjIqKilBUVGQa/+jRo4iKisK0adMwbdo0\nTJw4kWl8AOjatSuSkpLQp08f3L17F+3bt4dQKATHcUyeUgIDA7Fu3TqsXr0a+vr68Pf3R1JSElas\nWMHg7Cv/3WNjY/H555/j2rVrUFRUxO3bt1FWVsYkPgDExsbCzs4OZmZm1d53cXFhEt/AwAAJCQko\nKysDx3HIyspC3759mcQWkZOTw+nTp2FqaorLly8zfwK1sLBATEwMMjIyoK+vDzs7O6bxXVxcoKGh\nIZ4iKhAImCb0EydOYPXq1bxOu2vsaD90ItWCgoLw+PFj9OnTB0lJSejQoQOzRAVUdnDv378f06ZN\nw549e+Dg4ID9+/cziw8Ao0ePRlFRERQVFatNBRIIBIiPj2dyjPT0dKSnp0NHRwcGBgZMYopkZ2dj\n69atuHfvHvT19TFv3jz8/fff6NChA/T19Zkco6ysTHyTIEq4lpaWTGIDwJQpU6Cnp4fU1FQoKyuj\nadOmzKdRPX78GEFBQcjIyICenh6WL1/OtIvbxcUFenp66N27N65evYqsrCymQzdTpkxhNjOiJjt3\n7sSRI0egq6uLyZMn8zoFr7GihE6k3pkzZ5Ceno6uXbvC3Nycaey9e/fi2LFjePLkCfT19dG/f3/e\nlqd8+fIlNDQ0mDdmRUVF4eeff4axsTGSk5MxduxYTJ8+nekxzp8/L25MHDJkCNPYQGXjXVFREV68\neAGhUAgtLS2m08qcnJwQFRUFT09P+Pv7w9HRkZdFTfhs4HR0dKzWs/Dm1/W1Zs0ajB8/vtoUUT6a\nCP/++29EREQgJSWF95kBjQ2V3IlUOn36NIYOHYqYmBgAgJqaGp49e4aYmBimpcYpU6bAzMwM//zz\nD3R1dWFoaMgstsilS5fg5eWFZs2aIS8vD2vWrMGAAQOYxT98+DD2798vrgDY29szTegbNmxAWloa\nTExMEBMTg8TERCxfvpxZfKDyZufAgQNYuXIlvL29md9UycvLo6SkBEVFRRAIBOIZByzt2bMHR48e\n5a2Bk++hm8TERJw6dUr8tUAgwJkzZ+odV6S4uBgnTpzAoUOHwHEcXF1dmcWWFpTQiVTKzc0FULlh\nBJ8yMjIQEhKCjIwMGBgYYMWKFcwXu9i4cSP27dsHLS0tZGZmYtGiRUwTOsdx4t4CPvoMLl26JH6a\nnTlzJiZPnsw0PgA0bdoUQOVCP02bNmXeYe3k5IRdu3ZhwIABMDc3R58+fZjGB/7rx+CrgTMpKQkJ\nCQnVhm5GjRrFbOiG1ayIdxk7dixGjRqF1atXM58SJy0ooROpNH78eACVJb+qjVGspxqtWLECCxcu\nhImJCZKSkuDh4YHIyEimx5CXlxdPh9PS0mI+/7Z3795YvHixeMnOXr16MY0vGtcWJVnWyRaoXAVt\n8+bN0NfXh4ODA/ObklGjRolfW1pa8rLBTEM0cPJh7dq18Pb2hqOj41vf26ioKGbHOXbsGDIyMnD7\n9m0UFhbyvoNfY0QJnUil2NhY/PTTT0hPT8e5c+cAABUVFRAKhViyZAmz4zRt2lQ8Lj9kyBD8+OOP\nzGKLqKmpITIyEv/73/9w+fJl5l2+Xl5eOHXqFO7duwdra2sMHz6cafxRo0bB0dERvXv3xvXr16sl\nR1ZGjx6Nli1bQiAQwNzcHLq6ukziZmdnIzQ0FElJSSgpKUHbtm1hYmKCBQsWMF9Tv0+fPnBzcxM3\ncH7++edM4mZlZSE8PBzNmzfHiBEj4OrqCnl5eaxbtw69e/eud/w5c+YAgHgtA77s379fvKZEREQE\n8yEJaUBNcUQqlZaWIisrC9u3b8f8+fMBVD6tt2rViul0IA8PD3Tq1An9+/fHrVu3cO7cOfEa5ayW\nmH39+rW4S7xLly6YN28ek6T+008/vfMzW1vbesev6s6dO7h37x709PR4ebKaPn06OI7DsGHDMHLk\nSPEGM/W1cOFCTJkyBSYmJoiPj8fjx4/RuXNnHDt2DBs3bmRyjKpEDZxdunRh1jw4c+ZMjBkzBk+e\nPEFUVBT27t0LFRUVLF26lGlXemZmJoKCgpCWlgYdHR14eHgw3XfAzs7urSGJgwcPMosvDegJnUgl\nJSUlaGtrY9WqVbh586a47JuUlMR0rW+BQICHDx/i4cOHACqX1xSVNuub0KuuPz958mRx2To7O5tJ\nQn/06FG1rwUCAVje32/cuPGtEmxaWhp+//13fPXVV8yOA1SuR5+Xl4czZ85g8eLFEAqFTH7Z5+bm\niue2W1lZwdnZGZGRkdi5c2e9Y4tcvnxZ/FpVVVW8st3ly5eZ7IdeWloqHoJKTEwUb87CeujD29tb\nvC1uYmIis6WDRfgekpAGlNCJVHN1dYVQKERWVhbKy8vRpk0bpgk9MDAQ5eXl4DgOycnJMDY2ZlYB\nWLVqlTjJCgQCvHr1CvLy8lBTU2MyJevrr78Wv+ZjWtmbzYGsbxiqOn36NP78809cu3YNrVu3ZlYd\nUVVVxY4dOzB48GDEx8dDW1sbycnJTGKLvLlugUAgwF9//YXS0tJqyf5jqaurY+vWrViwYIF4Q5xf\nfvmFeS9GcXExRo4cCaByERvWc9L5GpKQJvztNEDIJyAnJwcREREwNjZGXFxctWUpWfD390dsbCzC\nwsKwbds2psu+enh44NWrV4iIiMCUKVOQlZWFgoICptuOApXTyqKjo1FRUYGYmBgEBwcziTtp0iRM\nmjQJEyZMQHl5OVJSUlBaWopx48YxiV9VYGAgEhMTMWvWLAQHB8PJyYlJ3ODgYOTk5CA0NBSlpaXw\n9vbGq1ev8O233zKJDwChoaHiP6tWrUJFRQX09fWZLFkLVDaCqqqqVnsiF5XHWSorK0NaWhqAyoWK\nWN68xcTEwN3dHRMmTMDr16/Rt29fpgtESQ2OECk2depUjuM4bvHixRzHcZyDgwPT+HZ2dhzHcdyU\nKVOqHY+FqVOncnfu3OE4juMsLS25mzdvcq9fvxYfk5Wq8SoqKjhbW1um8VeuXMmtWbOG++233zg/\nPz/Ow8ODaXyRf//9l9u7dy/n7OzM2dvb83KMN7m4uDCLdebMGW7kyJHc3r17mcWsC1bX8Pfff3Pj\nxo3jBg0axI0fP567desWk7hhYWGcq6srV1hYyHEcxz18+JBbuHAht3nzZibxpQmV3IlUGzlyJDZv\n3gwjIyNMnjyZyf7eVVVUVODmzZvQ1tZGaWkpCgoKmMY2MjJCZmYmioqK0KNHDwDsxz75nlaWkZEh\nnr5kYWEBe3t7pvEBICUlBefOnUNCQgIUFBTEpV++5eXl1TtGYWEhAgICkJ6ejvDwcHTq1InBmdUd\nq2swNDTEzz//zOCMqjt37hwOHDgg/rnU1tbGhg0bYG9vj4ULFzI/XmNGCZ1INUtLS7Rs2RIAYG5u\nznxBChsbG/j6+iIwMBBBQUEYNmwYs9iiBqDz58+LG7OEQiEKCwuZHQN4e1qZhYUF0/glJSUoKSmB\nsrIySkpKUFFRwTQ+UNmAN3LkSGzatAkaGhrM478Li5sfa2trlJSUwMbG5q2ZByy2N61Nfa9h3759\nCA8Ph4KCAnx9ffHFF18wOrNKKioqb52joqIi82mD0oASOpFqs2fPRseOHTF58mSmq6uJODk5oWfP\nnggPD0dCQgLTOdZmZmawt7fHs2fPsG3bNjx48AB+fn6wsrJidgygsooxcOBA3Lt3D2PHjmU+rWzK\nlCkYO3YsDA0N8c8//4j3zGZBtDqZlZUVBAIBEhISxJ+x3OmLT419CdPDhw/jxIkTyM/Px/Lly5kn\n9CZNmuDhw4fo2LGj+L2HDx/yskBRY0cJnUi1uLg43LhxA3FxcQgNDcWIESOYJJTS0lLxUp1KSkrI\nz89HfHw8mjRpwuCsK82dOxfDhw+HmpoatLS08ODBA9jZ2eHLL79kdgygsvlu//79vK281aFDB+zf\nvx8PHjxAp06dxBUTFm7fvg0AuHnzJpSUlPD555/j5s2bKC8vbzQJXTSl7F0WLlyILVu2NNDZfDgl\nJSUoKSmhZcuW1XYDZGXp0qVwcXGBmZkZOnbsiCdPniAhIYF5U580oIROpJ6+vj569+6NBw8e4MqV\nK0xiDhs2DNbW1ggJCYGOjg5mz57NNJmLdOnSRfy6U6dOvIyvqqioICgoCHp6euKnHpYLy2zcuBFR\nUVFME7mIqNN51qxZiIiIEL8/c+ZM5seqSUPszc1ijPt9WF4Dx8O0RH19fezbtw/x8fHIyspCjx49\nsHDhQl6W323sKKETqebp6SlebtTX1xfa2tpM4k6bNg2//vorHj9+DFtbW97mVzeEnj17Aqjcjxtg\n3xQnJycHNzc36OrqirfTZL2wTHZ2NvLz86GmpoZXr16JN+dhJTMzE8HBwcjOzoaFhQUMDQ3Rq1cv\nfPfdd0yPUxNW3w++riE9PR3Lly8Hx3Hi1yKspvc1a9aMl+mO0oYSOpFqX375JQICApgnqTlz5mDO\nnDlITExEbGwsbt68ieDgYNjY2MDAwIDpsfiSmZkJAHBwcOD1OGPHjuU1PlD5/RgzZgxatWqF3Nxc\neHl5MY3/zTffYMaMGdi6dStMTU3h4eGBAwcOMD0G3/i6hpCQEPHrCRMmvPV5WVmZuMGT8Iv+lYlU\n09bWhpOTE/Ly8jB27Fjo6+tj6NChzOL37dsXffv2RV5eHn755RcsX74chw4dYhafTy4uLuIV6AoK\nCtC1a1fcu3cPmpqaTK7h6tWrAKoPG/DFysoKX375JV68eIHWrVszTyDFxcUwMzPDtm3boKenx3yV\ntYbA1zWIZmC8y8yZM5msbEhqRwmdSDV/f38EBgbC29sbtra2mD17NtOELqKurg5nZ2c4Ozszj80X\n0Vrnrq6uCAgIQLNmzVBQUIClS5cyiS9ax/vRo0cQCoX47LPPcPv2bTRr1oz5sqBnzpzBvn37xHPq\nX758yWylNQBQVlbG+fPnUVFRgeTkZKYb/NSG1Ri3pK6hMQ9HNTa09CuRep07d4ZAIEDLli1p7moN\nnj59imbNmgGoXLs8KyuLSdywsDCEhYVBU1MTcXFxCAwMxMGDB3lpHly/fj3mzp2LVq1awcrKCoaG\nhkzjr1mzBnFxccjJycHOnTuxevVqpvGByiGQpUuXYubMmThw4ACuX78OAMzG6RviGmpC08saDj2h\nE6nWvHlzREdHo6ioCEePHoW6urqkT+mT079/f0ybNg2fffYZ/v77b+YVjOfPn4ub4URPz6y1adMG\npqam+OmnnzBp0iSmT+cA0LZtW2zYsIFpzDfxPU7fENdAJIsSOpFqAQEB+P7776GhoYGbN2/C399f\n0qf0yfn6669x69YtZGRkwNLSEp999hnT+OPHj4e1tTUMDQ1x9+5dXqaUKSoqIikpCUKhEBcvXkRO\nTg6TuKJd24RCIYqKitCuXTtkZmaiZcuW+OOPP5gcQ4SvMe6GvAYiWZTQiVSqupf4xIkTxa9zcnLQ\nokULSZzSJ2v8+PEYOHAgbG1teWlgs7a2xqhRo/Dw4UPo6OhAU1OT+TF8fHyQkZGBefPmYcOGDZg7\ndy6TuKKV55YuXYolS5aIk2FgYCCT+FXxNcbdkNdQEx0dnQY5DqGETqRU1W1MRWN4HMehtLQUtJYx\nXAAAHXxJREFUMTExkjqtT9KhQ4dw5swZhIaG4vXr1xg3bhwsLS3RtGlTJvFdXFzQpk0b2NraolWr\nVkxivqldu3ZIT0/Hn3/+CTs7O2Z7uos8evQI7dq1AwBoaWnh6dOnTOMDlWPcQUFBvI1x830N6enp\nWL16NV69eoUxY8bAwMAA5ubm8PPzY3oc8m4CjloQiRT66quvsGnTJgBAREQEZs2aBQBwdnZGZGSk\nJE/tk3XhwgXExMTg3r17UFNTw9ixY+Ho6Mgk9t27d3Hw4EEkJydj4MCBmDhxIjp06MAkNlC5p3ta\nWhpMTExw5coV6OnpYdmyZczir1y5EqWlpTA2Nsa1a9fQokWLajeNjQHf1zB9+nSsWrUKPj4+2LBh\nA+bNmyeeSUEaBj2hE6lUdQz17Nmz4oROHbdvCw0NxcmTJ9G7d29Mnz4dJiYmKC8vx4QJE5gl9A4d\nOqBr165ITU3FzZs3cePGDXTv3h2LFy9mEv/SpUuIjo4GUDnvefLkyUziiqxZswYnT57Ev//+Cysr\nK4wYMYJZ7IYa4+bzGoDKCpho+WBNTU3mWxWT2tG0NSKVqhaeqAj1fm3btkVsbCwCAwNhYmICAJCX\nl2c2XWrJkiWYNGkSsrKyEBgYiB07diA8PBznz59nEh/4b093EdY3bnl5eRAKhdDS0sLr16+xfft2\nZrETEhKQkJCAQYMG4cSJE+I/xsbGzI4B8HsNQOVaDLGxsSguLsbx48fFUyFJw6EndCKVqv5Cp6fy\nmn3//ffi128u9DJ//nxmG8GMHTsWISEhb30fWC4u8+ae7iy3sQWARYsWQU9PD6mpqVBWVmbWX1AV\n32PcfF+Dv78/tm3bhmbNmiEpKQlr165lGp/UjhI6kUppaWlYsmQJOI6r9jo9PV3Sp/bJqPoEtWfP\nHkydOpVpfG9vb3ESP3XqVLXP1qxZw7QkO2fOHF73dOc4Dn5+fvD09IS/vz+zoYiqunTpgmXLlonH\nuHv06ME0Pt/XsGbNGgQHBzONST4MJXQilTZu3Ch+bW9vX+NrWefk5CR+ffz48Wpfs1B1jHb9+vVY\nsmQJ0/gAkJ+fj7i4OKirq8PGxgbdunVDWloaHB0dsW/fPmbHkZeXR0lJCYqKiiAQCFBeXs4stgjf\nY9x8X0NRURHS0tKgo6MjXkhI9L+kYVBCJ1Kpb9++kj6FRoWPYYmqU8ciIiKYTyUDKmczGBoaIjk5\nGU+ePEHr1q2xadMmZuvRizg5OWHXrl0YMGAAzM3N0adPH6bxgZrHuOfNm8csPt/XkJGRIW4+BSp/\nps6cOcP0GOT9KKETQnjHVx/D69evsXz5clRUVMDS0hJt27bFzz//jNatWzM9TtUxeUtLS6ipqTGN\nD/A/xs33NRw9epRpPPLhKKETIqMcHR0hEAjAcRxSU1Ph5OQEjuMgEAgQFRUl6dOrE9HyqHJyclBS\nUsL27dt52fwlNjYWu3btQnFxsfi9+Ph4psfge4ybr2tYu3YtvL29xT9PVTWWnyNpQQmdEBm1bt26\n937+7NkztG3b9qPjm5ubi28YsrOzMWTIEPENA6tSbNUEoqGhwUsyB4D9+/djx44dzJ/8q+J7jJuv\na5gzZw6A2n+eCP8ooRMio2qblrZ8+XLs2bPno+PXtijKjRs30LNnz4+ODwC3b98WVxb4rDJoaGgw\nXdmuJnyPcfN1DeHh4fD29kanTp2QkpICIyMj5scgdUMJnRBSo/ouyCMvL//ez4ODg+t1wwAAcXFx\n7/28vlWG0NBQAEBpaSlmzZqF7t27i6sC7u7uHx23JnyNcfN9DampqeLXAQEB9f6eko9HCZ0QUiO+\nF+RhsYIf31UGXV3dav/LJ77GuPm+BlqV8dNBCZ0QIhENsYJffRPM+PHjAQCFhYXIy8uDvLw8Dhw4\ngHHjxrE4vWr4GuPm+xpoVcZPB836J4TUSBqetlglGDc3N9y6dQvBwcFQVFTkZac10Ri3kpKS+A9L\nfF3DtWvXMGTIEJibm1d7zce6A+T96AmdEAIAePXqFdTV1cVJkO/FeRrTDUNxcTGGDRuG3bt349tv\nv8Wff/7JLHZDjdPzdQ3Jycnv/by+fQyk7iihEyLjLl++DC8vLygrK6OwsBD+/v4wMzODq6srs2O8\nfv0aDx8+RIcOHdC8eXMAgJWVFbP478LqpkEoFGL37t3o0aMH0tLSUFRUxCQu0HDj9HxdQ23Nj/Xt\nYyAfgCOEyDR7e3vu6dOnHMdx3JMnTzhbW1um8Y8fP859+eWX3Ny5c7lhw4Zxv/76K9P4VeXm5nIV\nFRXir8PCwpjETUpK4oKCgrhXr15xkZGR3PXr15nEraqgoIB7+vQpl5WVxW3evJl79OgR0/gNcQ01\nmTJlSoMch3AcPaETIuPk5eXFJdF27dqJV19jZefOnYiLi4Oamhry8/Mxbdo0WFtbMz0GX1WGjIwM\nAJXj25MmTcLLly8xYMAAFqf8Fjc3Nzg4OODEiRPo2rUrVq1ahYiIiHrHbchrqAk1yjUcSuiEyDgV\nFRXs27cP//vf/3D58uVq26qyICcnJ55TraamxstqbqGhoYiMjETbtm3x9OlTuLm5ITY2tt5x39U4\nJhAImJeR+RrjbshrIJJFCZ0QGRccHIwtW7YgPj4eXbt2RUBAANP47du3R3BwsPiGgY/VyviqMkRG\nRjKJUxd8jXE35DXUhGtEzY+NnYCjf21CZBLr7TnfRSgUYt++fUhPT0eXLl3g4ODAfErW3LlzMWTI\nEPFNw/nz57Ft27Z6xx04cOA7P0tISKh3/KquXr2KU6dOYf78+Th8+DCMjY1hbGxc77gNeQ3A27Ml\nvvvuO6YNluTdKKETIqOmTp3Ka8lVtAtXQ3j16hW2bNmC9PR0dO3aFfPnz4eGhkaDHLu+RGPcNWmI\nFepYeVcfA2k4VHInREbl5ua+8wntfU91dVV1jW++iKoMzZs3h5eXF/P4W7duhYuLC9zd3d9q7lq/\nfj2TY/A9xt0Q1wDw18dA6o4SOiEyKjs7G0ePHq3xMxYJPTMzEzExMTV+ZmdnV+/4AHDhwgVehw1U\nVVVx6NAhDBo0SLwVLMC2c5vvMe6GuAaA/9kSpHaU0AmRUbq6uggMDOQtvlAoxPPnz3mLD/BfZXjx\n4gVevHgBADh69Cisra3F27OywvcYd0NcA8D/bAlSOxpDJ0RGTZ8+Hbt27eItvrOzM+9PnwMHDsSg\nQYNq/Iz1zUpDXA/f+LyGxtzHIC3oCZ0QGVVbMvfx8YGvr+9Hx9fS0vro/7au+K4yVMXXAikNNcYN\n8HMNfPcxkLqjhE4IqdH7uq/rIiQk5L2fL1y4EFu2bKnXMWpbR7wxaKgxbr7w3cdA6o4SOiFEIvLy\n8uodg+8qg+ipmeM4pKWlYcmSJeLPWD098z3Gzfc18N3HQOqOEjohRCIa4gm0vlUGe3v7Gl+zVDXB\nJicnM90yFeD/GvieLUHqjhI6IYS8A997wr+Jj5scvq+hIfsYyPvJSfoECCGfJpoAQ+pCGvoYpAU9\noRMi4/Lz8xEeHo6srCwMHToUhoaG6Ny5M3bu3MnrcZs3b85r/MaiIcbp+cR3HwOpO0rohMg4Ly8v\nDB48GJcvX4ampiZWrlyJvXv3QlFRkUn8zMxMBAcHIzs7GxYWFjA0NESvXr3w3XffMYn/Po2hytAQ\n4/SSVN8+BlJ3lNAJkXG5ubmwtbXF4cOHYWJigoqKCqbxv/nmG8yYMQNbt26FqakpPDw8cODAAabH\nkFSVgYWGHqcn0ovG0AkhSE9PBwA8e/aM+ZhocXExzMzMIBAIoKenx8sa315eXujYsSPu378vrjIA\nYFZlIKQxoIROiIzz9vaGl5cXbt++DTc3N3h4eDCNr6ysjPPnz6OiogLJycnM90IH/qsyKCgo8FJl\nIKQxoJI7ITJOR0cHPj4+6N69O06dOgUDAwOm8desWYOgoCDk5ORg586dWL16NdP4InxWGcjHawx9\nDNJCfjVf/+8ihDQKixcvhqqqKrp37474+HhER0dj1KhRzOILhUK0bt0aHh4eePnyJXr16sW87N6j\nRw94e3sjNTUVf/31F7y9vdGmTRumxyDvl5+fjy1btuDw4cOoqKiAgoICWrRogTFjxtANVgOhkjsh\nMi4zMxMTJ04EAMyZMwdZWVlM47u7u6O0tBRA5VS1ZcuWMY0P/FdluHLlCubOncu8ykBqR30MkkcJ\nnRAZJxAIxFOLHjx4wHz8uaioCEOHDgUAjBkzBkVFRUzjA8DSpUtx584dAJXTpFj3AZDaUR+D5NEY\nOiEyztPTE4sXL8aLFy/Qpk0b5ouAKCoq4sKFC+jVqxdu3LgBOTn2zxFvVhmcnZ2ZH4PUjvoYJEvA\nUccCIYRH9+/fR1BQEDIyMtC1a1csW7YMnTp1YnoMe3t7BAYGQldXFw8ePICnpyeioqKYHoO8X2pq\nKr755hukp6dDT08PPj4+6NGjh6RPS6ZQQidExh06dAg7duxASUmJ+L34+HgJntGHu379Onx8fKpV\nGXr27Cnp05IppaWlSEtLE8+WMDc3p/HzBkYJnRAZN3r0aGzduhXt2rUTv8dyrvj333+PH374AU2a\nNBG/9679s0nj5ebmBnNzc0ycOBHh4eFISUlpFGvRSxMaQydExnXs2BGdO3fmLf6xY8dw/vx5NG3a\nlLdjSEOVobGjPgbJo4ROiIxr0qQJZs+ejW7duon343Z3d2cWX1tbu9rTOR/Cw8Oxbdu2alUG0rBE\nsyVEfQzU5d7wKKETIuPMzc15jS8UCjFmzBjx3HCBQMC8FMt3lYHUju/ZEqR2NIZOiIwrKyvDjRs3\nUFZWBo7jkJWVBWtra2bxExMT33qP9Q5jX3/9NfLz83mrMhDSGNATOiEybtGiRRAKhcjKykJ5eTna\ntGnDNKEbGBggISGh2g0D64TOd5WB1I76GCSPEjohMi4nJwcxMTFYuXKleO9ylhYtWgQ9PT2kpqZC\nWVmZl+a4MWPGvFVlIA2L+hgkjxI6ITJO1LBWVFSEJk2aiEvWrHAcBz8/P3h6esLf3x+Ojo5M4wP8\nVxlI7aiPQfJoLXdCZNzIkSOxZcsWGBkZYfLkycz3K5eXl0dJSQmKioogEAhQXl7OND5QWWWIiIiA\nsbEx4uLiqpV9ScMQzZZYv349QkNDERoaKulTkjn0hE6IjBs+fDi0tLQgEAhgbm4OBQW2vxacnJyw\ne/duDBgwAObm5ujTpw/T+AD/VQZSO+pjkDzqcidERqWmpiIzMxMhISHiLU3Ly8sRGhqKX375hdlx\nbty4IV6GNT8/H7dv32beFBcVFYXc3FwoKiri1KlTUFFRwa5du5geg7wf37MlSO3oCZ0QGZWXl4dj\nx47h5cuXOHr0KIDKOeKsxrivXLmCtLQ07Nq1S9xoV1FRgaioKBw5coTJMUT4rjKQ2lEfg+TRTz0h\nMsrU1BSmpqa4deuWeFesiooKZtubqqur48WLFygtLcXz588BVN4wiKoBLDRUlYHUju/ZEqR2lNAJ\nkXHp6en4999/UVpaiuDgYMyaNQuzZs2qd1wDAwMYGBhg0qRJ0NLSAgA8ffqU6bQmvqsMpO6oj0Hy\naAydEBlna2uL8PBwuLu7Y/v27Zg5cyb27t3LLP4PP/wAdXV15OXlIS4uDoMGDYKnpyez+AB4qzKQ\nuqM+BsmjJ3RCZJyysjIAQFVVFUpKSigrK2Ma//fff8fevXsxe/ZsHDt2DFOnTmUaH+CvykDqjvoY\nJI9uYwmRcZ06dYKdnR0mTpyIzZs3w9DQkGl8OTk5vHjxApqamgCA4uJipvEBYM+ePfjiiy9w+PBh\nnDlzBqdPn2Z+DFKz1NRUnD9/HvPmzcOFCxeQkJCAZ8+e0Vr6EkC3UITIuMDAQBQUFEBVVRU9e/YU\nJ15W+vXrB2dnZwQHByMgIICX+cp8VxnIu1Efw6eDxtAJkVFbt26Fi4sL3N3d32pgYr29qYhQKISi\noiLzuJ6enkhKSoKnpydu3bqF58+f0/adDYz6GCSPEjohMiolJQVGRka8bW/q5+eHVatWwc7O7q0b\nhujo6HrHf5OoylC1vE8azuHDhyEvL099DBJECZ0QGZaSkoITJ04gJycHbdu2hYWFBXR0dJjEFiXW\nx48fv/VZhw4dmBxDElUGUjO+Z0uQ2tEYOiEy6rfffkN4eDjs7e3x2Wef4cmTJ3Bzc4ObmxtGjBhR\n7/iampo4c+YMjh07Jr5hsLKygpmZGYOzrzRs2DAAgL29PbOY5ONQH4PkUUInREbt2bMHe/fuhYqK\nivi98ePHY8GCBUwSelRUFM6dO4epU6eiVatWePLkCbZv344HDx7Azs6u3vEBwMjICCkpKbh48SIv\nVQZSd6LZEp6enrzMliC1o4ROiIxSUFColswBQE1NDfLy8kzi//rrr4iKihLHMzIywsCBAzFz5kxm\nCZ3vKgOpO75nS5DaUUInREa9a2nOiooKJvEVFRXfujlQUlJidsMA8F9lILWjPoZPByV0QmRUWloa\nlixZUu09juOQnp7OJP67bhhY9uHyXWUgtaM+hk8HJXRCZNTGjRtrfF/0i7m0tBRKSkofHf/WrVtv\n/ZJnecMA8F9lILWjPoZPB01bI4TUaOrUqdizZ89H//c1TVcT6dChAx4/flzv6WtffPHFW13zHMfh\n0qVLuHDhQr1ik7qp2scgan6MjY2lPgYJoIROCKmRs7MzIiMjeYtf3xsGADUuiiPSt2/felcZSO0c\nHBwQERFRbegjPz8fCxYs4PXnh7yNSu6EkBrxvZ81i2eJ2la0mz17dr1vGsj7UR/Dp4MW2yWESATf\nNwwA2wY8UjPqY/h00BM6IaRG0pAMG+KmQdbxPVuC1B0ldEJIjbp27cprfGm4YSD8z5YgdUcJnRAZ\nVdNCICLr16+Hj48Pr8fv378/r/EBumloCNTH8OmghE6IjOJ7IZCatk3lOA4CgQDR0dFYuHAhr8cH\n+K8ykNrRTVXDoWlrhMi43NxcJCQkoKysDBzHISsrC/Pmzat33NrmobNQW5WBSB6L6YmkbugJnRAZ\nt2jRIujp6SE1NRXKyspo2rQpk7iipH3//n0cP34cQqEQAJCVlQU/Pz8mx6DlRgn5D01bI0TGcRwH\nPz8/6Orq4scff0Rubi7T+KIO6KtXr+LRo0dM4/ft2xd9+/aFgYEBsrKy8OTJEzx+/BjXrl1jdgxS\nP1QEbjj0hE6IjJOXl0dJSQmKioogEAhQXl7ONL6KigrmzZuHf//9F4GBgXB0dGQaH+CvykDqj/oY\nGg4ldEJknJOTE3bv3o0BAwbA3Nwcffr0YRpfIBDg+fPnKCgoQGFhIQoLC5nGB/6rMnh6esLf35+X\nmwZSM0nPliD/oYROiIxr3749Ro0aBQCwtLTE7du3mcZftGgRTp06BRsbG4wYMQI2NjZM4wP8VxnI\nu1Efw6eDEjohMurKlStIS0vDrl27MGPGDACVy3VGRUXhyJEjzI7Tq1cvqKmpoVu3buA4Dubm5sxi\ni/BdZSDvJpqHXtNsidrmqBO2KKETIqPU1dXx4sULlJaW4vnz5wAqy+PLli1jepylS5fC3Nwc3bp1\nQ0ZGBn777TfmU8r4rjKQ2lEfg+RRQidERhkYGMDAwACTJk2ClpYWb8fJzMzExIkTAQBz5syBs7Mz\ns9gNVWUgtaM+BsmjhE6IjLt48SK2b9+O0tJS8Upu8fHxzOILBAJkZGRAV1cXDx48YLoLV0NVGUjt\nqI9B8milOEJk3OjRo7F161a0a9dO/B7LzTSuX78OHx8fvHjxAm3atIGvry969uzJLD5QWQXgs8pA\nanfixAncv38fGhoa+O6779CnTx9s2LBB0qclU+gJnRAZ17FjR3Tu3Jm3+L169cKhQ4d4iw/wX2Ug\ntaM+BsmjJ3RCZNzXX3+N/Px8dOvWTTyf2N3dvd5x3dzcEBYWhoEDB771WUJCQr3jV8V3lYG8G/Ux\nfDroCZ0QGcfHNDIACAsLAwDExsZWS7Tp6enMj8V3lYG8G/UxfDroCZ0QGVdWVoaYmBikpaVBR0cH\nDg4OTJ5uU1NTkZmZiZCQECxfvhwcx6GiogLr16/HL7/8wuDM/8NXlYHUHfUxSB49oRMi41atWgV1\ndXUMGDAAiYmJ8Pb2xrffflvvuHl5eTh27BhevnwpLr0KBAJepjPxVWUgdUd9DJJHT+iEyDgnJydE\nRUWJv7a3t0d0dDSz+Js3b8aiRYuYxasJX1UGUnfUxyB5tH0qITJONHcYAIqLi5nPH/7rr7+YxqvJ\nqlWr8PDhQwwYMACPHz+Gt7c378ck1Yn6GJSUlMR/SMOikjshMm7q1KmwsbGBvr4+0tLS4ObmxjR+\naWkpxo0bB11dXcjJVT5DsF769f79++Iqw4gRI2jDEAlo0qQJZs+eTX0MEkQJnRAZN2rUKAwePBgP\nHz6EtrY2srOzmcZfunQp03g1EVUZmjZtykuVgdSO+hgkj0ruhMi4/v3748aNG+jZsyc0NDTg6+vL\nNH737t1x4cIF/Pzzz8jNzeWlE1pUZVi4cCFsbGwwffp05scg7zdmzBgUFhbi77//Rl5eHkaPHi3p\nU5I5lNAJkXF6enrYtWsXDh8+DKBykw2WvLy80LFjR9y/fx+amppYuXIl0/hAZZXhwIEDmD9/PqKj\no2FkZMT8GOT9qI9B8iihEyLjVFVVsW3bNvz+++/YuXMnFBUVmcbPzc2Fra0tFBQUYGJiwnRzFhG+\nqwykdvfv34eHhwdGjBgBLy8vPHjwQNKnJHMooRMi4ziOg5KSEjZt2oS7d+8iOTmZ+TFEq8M9e/YM\n8vLyzOPzXWUgteN7tgSpHTXFESLjAgMDAVRufxkUFIShQ4cyifvTTz/B2toa3t7e8PLyQnp6Otzc\n3ODj48MkflWiKoO7uztevHjBvMpAasf3bAlSO0rohMiorVu3wsXFBaGhoeJpRiIWFhb1jn/37l1s\n374dAwYMgK+vL6/j2lWrDF5eXrxUGcj78T1bgtSOVoojREalpKTAyMgIp06dgrq6erXP+vbty+QY\nQqEQ8fHxiIuLQ15eHiZOnAhra2s0bdqUSXyRR48eQVtbW/z18ePHmdyUkLr7/PPPERYWhkGDBgGo\nfGLfs2ePhM9KttATOiEySvTEHBERgf379/NyDEVFRVhYWMDCwgKZmZmIjIzEkCFDcOnSJSbx+a4y\nkLoT9THk5ORg7Nix1McgAZTQCZFxzZs3x+7du6ut5FbTHuYfq6SkBCdPnsShQ4dQUFDAdFvNYcOG\nAQCsrKzeqjKQhkV9DJJHCZ0QGaehoYGUlBSkpKSI32OR0C9duoRDhw7h0qVLGD58OJYvXw4DA4N6\nx62qIaoMpG6oj0HyaAydEILU1FSkpaVBV1cX3bp1YxLT2dkZkydPxqhRo3jfqGP+/PkwMzPjrcpA\nakd9DJJHCZ0QGRcZGYkjR47A2NgY165dg6WlJWbNmiXp0/ognp6eb70nmo5H+CXqY3B3d3+rj4H1\nJjzk/SihEyLj7OzsEBUVBQUFBQiFQtjb2+PgwYOSPq0PxkeVgdSuIWZLkLqhMXRCZBzHcVBQqPxV\noKio2CibmapWGXbu3NkoqwyNFfUxfDoooRMi4/r06QM3Nzf06dMHSUlJMDExkfQpfbAjR468VWWg\nhN6w+J4tQWpHCZ0QGXXo0CEAgKGhIbS1tVFSUoJ+/fpBVVVVwmf24aShytDY8TVbgtQdJXRCZJRo\nwxQAOHr0KKytrcFx3FuNTY2BNFQZGrvAwEDqY5AwaoojhMDZ2RmRkZGSPo0PJqoyAEBBQQFKSkqg\nrKwMVVVVjBs3ToJnJnukYbZEY0dP6ISQRvlUDkhXlaGxoz4GyaOETghptJYsWSJ+nZycDHd3dwme\njWyjPgbJo4ROiIwSLQTCcRzS0tKqJcfGuCAIPZVLFvUxSB4ldEJklL29fY2vCfkQ0jRborGjhE6I\njJKGVbykrcrQGFEfw6eDutwJIY1WYmLiOz+ThhuWxqaxzpaQFvSETghptChpf1roqVyy5CR9AoQQ\nQgipPyq5E0II+WhV+xj++usvmJmZiT+jPoaGRQmdEELIR6M+hk8HJXRCCCFECtAYOiGEECIFKKET\nQgghUoASOiGEECIFKKETQgghUoASOiGEECIF/g9q53koFcRvggAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x115eb4358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Correlation matrix with all our numeric data plus 'Cover'.\n",
    "# N.B. We don't really expect any correlation with 'Cover' because it is categorical.\n",
    "\n",
    "corr_cols = raw_data.columns[:10].tolist() + ['Cover']\n",
    "corr_mat = raw_data.loc[:, corr_cols].corr()\n",
    "sns.heatmap(corr_mat)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This looks good -- there are a few places with high correlation (Vertical and Horizontal distance to water, Aspect/Slope and Hillshade), but for the most part these variables are all fairly independent. That should allow us to extract the most possible information for our models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preparing the Data for Processing\n",
    "\n",
    "Let's go ahead and create a feature for the cosine of the Aspect. Our 'Soil_Type' and 'Wild_Area' are already one-hot encoded, so nothing to do there. We'll need to separate our train/test data and resample the rare classes in the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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u/sipVsqgVqoR7t/5Mbhr9RA3ZZ2Hzd4S8E1WO5ptDtw7KBjGZhvsDgHC5Z6z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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1157d3cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a new column for the cosine of the Aspect.\n",
    "\n",
    "raw_data['Aspect_Cos'] = np.cos(np.radians(raw_data.Aspect))\n",
    "sns.distplot(raw_data.Aspect_Cos)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking the cosine clumps the data at the extremes as part of the transformation -- this isn't ideal, but let's leave it in along with unaltered Aspect and see how it goes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.utils import resample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split the Data\n",
    "\n",
    "We'll use a test size of 30% the entire set -- this should preserve each of the seven classes in both the training and test sets, but we'll double check anyway."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Split the data frame first.\n",
    "\n",
    "data_train, data_test = train_test_split(raw_data, test_size=0.3)\n",
    "data_train_nosamp = data_train.copy()\n",
    "data_test_nosamp = data_test.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's see if we've gotten all our classes in the test set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    63596\n",
       "2    84957\n",
       "3    10679\n",
       "4      816\n",
       "5     2889\n",
       "6     5230\n",
       "7     6137\n",
       "Name: Cover, dtype: int64"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the value counts for each output class.\n",
    "\n",
    "data_test.Cover.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great -- it looks like we do have enough of each so that random sample will pretty much always grab every cover type. \n",
    "\n",
    "### Resampling\n",
    "\n",
    "On to resampling the training set. Let's resample all but the most frequent class up to the size of the most frequent class (which is 'Cover' = 2, if you're keeping track). This will increase the size of our training set by about 3-4 times."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    148244\n",
       "2    198344\n",
       "3     25075\n",
       "4      1931\n",
       "5      6604\n",
       "6     12137\n",
       "7     14373\n",
       "Name: Cover, dtype: int64"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_train.Cover.value_counts().sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1    198344\n",
      "2    198344\n",
      "3    198344\n",
      "4    198344\n",
      "5    198344\n",
      "6    198344\n",
      "7    198344\n",
      "Name: Cover, dtype: int64 \n",
      "\n",
      "Total Length: 1388408\n"
     ]
    }
   ],
   "source": [
    "# First, separate out the different classes. We'll use a dictionary to store our temporary dataframes.\n",
    "\n",
    "frames = {}\n",
    "\n",
    "for cover_type in range(1,8):\n",
    "    frames['df_' + str(cover_type)] = data_train[data_train.Cover==cover_type]\n",
    "\n",
    "# Resample all to the length of df_2.\n",
    "\n",
    "for cover_type in [1, 3, 4, 5, 6, 7]:\n",
    "    frames['df_' + str(cover_type)] = resample(frames['df_' + str(cover_type)], n_samples=len(frames['df_2']))\n",
    "\n",
    "# And recombine.\n",
    "\n",
    "data_train = pd.concat([frames['df_1'], frames['df_2'], frames['df_3'], frames['df_4'], \n",
    "                        frames['df_5'], frames['df_6'], frames['df_7']]).sample(frac=1)\n",
    "\n",
    "print(data_train.Cover.value_counts().sort_index(), '\\n\\nTotal Length:', len(data_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity check on array shapes:\n",
      " (1388037, 55) (1388037,) (174304, 55) (174304,)\n"
     ]
    }
   ],
   "source": [
    "# And finally, we'll create our X and Y arrays for processing.\n",
    "\n",
    "X_train = data_train.loc[:, ~data_train.columns.isin(['Cover'])]\n",
    "Y_train = data_train.Cover\n",
    "X_test = data_test.loc[:, ~data_train.columns.isin(['Cover'])]\n",
    "Y_test = data_test.Cover\n",
    "\n",
    "print('Sanity check on array shapes:\\n', X_train.shape, Y_train.shape, X_test.shape, Y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection\n",
    "\n",
    "Let's take a look at how our features behave, and see how using selected features compares to using the whole feature set. We'll try out RFE, Select K Best, feature importances from Random Forest, and PCA. Let's start with feature importances to get a sense of how many features we'll want to keep."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Let's just get all the sklearn stuff out of the way.\n",
    "\n",
    "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.naive_bayes import BernoulliNB, GaussianNB\n",
    "\n",
    "from sklearn.model_selection import cross_val_score, GridSearchCV\n",
    "from sklearn.feature_selection import RFE, SelectKBest\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "from sklearn.metrics import confusion_matrix, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.93211286029\n",
      "[[58528  4563     9     0    38    14   222]\n",
      " [ 3697 80555   298     3   212   212    33]\n",
      " [    6   181 10251    51    14   266     0]\n",
      " [    0     1   128   657     0    21     0]\n",
      " [   42   576    44     0  2215     8     0]\n",
      " [   10   223   591    25     6  4381     0]\n",
      " [  308    31     0     0     0     0  5884]]\n"
     ]
    }
   ],
   "source": [
    "# We'll run an unmodified Random Forest to get feature importances. \n",
    "# This will also be a good way to see where we're starting from with regards to model accuracy.\n",
    "\n",
    "rfc = RandomForestClassifier()\n",
    "rfc.fit(X_train,Y_train)\n",
    "Y_pred = rfc.predict(X_test)\n",
    "print(rfc.score(X_test, Y_test))\n",
    "print(confusion_matrix(Y_test, Y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ClJCQoAYNGrjbN2zYoAULFshut2vAgAF66qmnJEn9+vVTWFiYJKlevXpKSkr6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\nAAAABATCDwAAAICAQPgBAAAAEBD+H8GMmWxL6H7TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1158796d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Extract the top X feature importances. With only 55 features, we'll visualize all of them.\n",
    "\n",
    "importances = rfc.feature_importances_\n",
    "indices = np.argsort(importances)[::-1]\n",
    "top_indices = indices[:55]\n",
    "\n",
    "# Plot the feature importances\n",
    "\n",
    "plt.figure(figsize=(14, 4))\n",
    "plt.title(\"Top {} Feature Importances\".format(len(top_indices)))\n",
    "plt.bar(range(len(top_indices)), importances[top_indices],\n",
    "       color=\"r\", align=\"center\")\n",
    "plt.xlim([-1, len(top_indices)])\n",
    "plt.ylabel('% of Decisions Accounted For')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Percentage of decisions accounted for with 5 features: 0.5243352424693267\n",
      "Percentage of decisions accounted for with 10 features: 0.7166536608947304\n",
      "Percentage of decisions accounted for with 15 features: 0.8513172868550262\n",
      "Percentage of decisions accounted for with 20 features: 0.9223587635918397\n",
      "Percentage of decisions accounted for with 25 features: 0.954443839311077\n",
      "Percentage of decisions accounted for with 30 features: 0.9754263739840581\n",
      "Percentage of decisions accounted for with 35 features: 0.9885622345539569\n",
      "Percentage of decisions accounted for with 40 features: 0.9960070441251646\n",
      "Percentage of decisions accounted for with 45 features: 0.9989828072073453\n",
      "Percentage of decisions accounted for with 50 features: 0.999853281933548\n",
      "Percentage of decisions accounted for with 55 features: 1.0000000000000002\n",
      "Total Features: 55\n"
     ]
    }
   ],
   "source": [
    "# And let's see what percentage of decisions are accounted for with the top X features.\n",
    "\n",
    "for n_idx in range(5, 56, 5):\n",
    "    print('Percentage of decisions accounted for with {} features: {}'.format(n_idx, importances[indices[:n_idx]].sum()))\n",
    "print('Total Features: ' + str(len(indices)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's reduce to 25 features since we could potentially account for 95% of the decisions from the Random Forest. What are these features in the real world?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Elevation', 'Horizontal_Distance_To_Roadways',\n",
       "       'Horizontal_Distance_To_Fire_Points', 'Wild_Area_4',\n",
       "       'Horizontal_Distance_To_Hydrology', 'Vertical_Distance_To_Hydrology',\n",
       "       'Hillshade_9am', 'Hillshade_3pm', 'Aspect_Cos', 'Soil_Type_10',\n",
       "       'Aspect', 'Hillshade_Noon', 'Slope', 'Soil_Type_38', 'Soil_Type_39',\n",
       "       'Soil_Type_03', 'Wild_Area_3', 'Wild_Area_1', 'Soil_Type_40',\n",
       "       'Soil_Type_02', 'Soil_Type_04', 'Soil_Type_30', 'Soil_Type_22',\n",
       "       'Soil_Type_12', 'Soil_Type_17'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_indices = 25\n",
    "X_train.columns[indices[:n_indices]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Neat -- all of our numeric features made it near the top, with only two binary features doing that well (Wild_Area_4 and Soil_Type_10). After that, a smattering of the most revealing soil types and wilderness areas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1388037, 25) (174304, 25)\n"
     ]
    }
   ],
   "source": [
    "# And now choosing those best features to be in our modified feature set:\n",
    "\n",
    "X_train_rfc = X_train.loc[:, X_train.columns[indices[:n_indices]]]\n",
    "X_test_rfc = X_test.loc[:, X_train.columns[indices[:n_indices]]]\n",
    "print(X_train_rfc.shape, X_test_rfc.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Other Feature Selection Techniques\n",
    "\n",
    "We'll also make training sets using RFE, Select K Best, and PCA. We'll use 25 features for all of them for consistency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Selection with RFE:\n",
    "\n",
    "rfe = RFE(estimator=RandomForestClassifier(), n_features_to_select=n_indices)\n",
    "rfe.fit(X_train, Y_train)\n",
    "X_train_rfe = rfe.transform(X_train)\n",
    "X_test_rfe = rfe.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Selection with Select K Best:\n",
    "\n",
    "skb = SelectKBest(k=n_indices)\n",
    "skb.fit(X_train, Y_train)\n",
    "X_train_skb = skb.transform(X_train)\n",
    "X_test_skb = skb.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Decomposition with PCA:\n",
    "\n",
    "pca = PCA(n_components=n_indices)\n",
    "pca.fit(X_train)\n",
    "X_train_pca = pca.transform(X_train)\n",
    "X_test_pca = pca.transform(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection Comparison\n",
    "\n",
    "Now let's see how they all compare! Again, we'll test them out with an unmodified Random Forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scores with all features: \n",
      "[ 0.98709767  0.98714092  0.98717816  0.98697628]\n",
      "Scores with feature importances: \n",
      "[ 0.98699673  0.98718707  0.98770303  0.98744636]\n",
      "Scores with RFE: \n",
      "[ 0.9876283   0.98753313  0.9874925   0.98770879]\n",
      "Scores with Select K Best: \n",
      "[ 0.9880378   0.9880205   0.98821348  0.98836633]\n",
      "Scores with PCA: \n",
      "[ 0.98784458  0.98820795  0.98803179  0.98814138]\n"
     ]
    }
   ],
   "source": [
    "# Simply taking the cross validation score of each training set, using random forest.\n",
    "\n",
    "print('Scores with all features: ')\n",
    "print(cross_val_score(rfc, X_train, Y_train, cv=4))\n",
    "\n",
    "print('Scores with feature importances: ')\n",
    "print(cross_val_score(rfc, X_train_rfc, Y_train, cv=4))\n",
    "\n",
    "print('Scores with RFE: ')\n",
    "print(cross_val_score(rfc, X_train_rfe, Y_train, cv=4))\n",
    "\n",
    "print('Scores with Select K Best: ')\n",
    "print(cross_val_score(rfc, X_train_skb, Y_train, cv=4))\n",
    "\n",
    "print('Scores with PCA: ')\n",
    "print(cross_val_score(rfc, X_train_pca, Y_train, cv=4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It actually looks like we did better on cross val with Select K Best and PCA than we did with our full feature set. This may be because overfitting was reduced. We'll train our models on the full data set, and then use these engineered feature sets to deepen the analysis.\n",
    "\n",
    "### Choosing Select K Best\n",
    "\n",
    "Let's use SKB as our main feature set for that deeper analysis. It's likely these feature selection techniques (aside from PCA) are choosing most of the same features, so in some sense it doesn't really matter which we pick (in fact, we'll see later on that we can drop all the Soil Type features with almost no reduction in accuracy). I'm going to avoid PCA for this one because it will obscure which features were chosen. RFE and RFC feature importances would work just as well for the analysis. One advantage of SKB is that it is fast to run, so we'll save time if we need to update it later (or re-run our notebook). \n",
    "\n",
    "## Model Selection\n",
    "\n",
    "Let's run our data through several models, tune parameters for each, see how they compare, and choose the best one(s) to move forward with. We'll take a look at Logistic Regression, K-Nearest Neighbors, Naive Bayes, SVC, Decision Tree, Random Forest, and Gradient Boosting. We'll also keep track of how long each takes to run so we don't get stuck with a behemoth. We'll save most of the analysis for the next section and just run the models here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# We'll use datetime to measure our processing speed, and a couple functions to cut down on typing.\n",
    "\n",
    "import datetime\n",
    "\n",
    "def t_now(): return datetime.datetime.now()\n",
    "def t_dif(tic): return str(t_now() - tic)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.677025310341\n",
      "Best Parameters:  {'C': 100, 'penalty': 'l1'}\n",
      "[[40693 10056    65     0  4117   586  7897]\n",
      " [19758 39979  2339    39 18584  3614   842]\n",
      " [    0    47  5972  1505   403  2784     0]\n",
      " [    0     0    46   698     0    55     0]\n",
      " [   60   419   223     0  2024   149     0]\n",
      " [    0   181  1003   527   408  3080     0]\n",
      " [  712    18    26     0    17     0  5378]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.66      0.64      0.65     63414\n",
      "          2       0.79      0.47      0.59     85155\n",
      "          3       0.62      0.56      0.59     10711\n",
      "          4       0.25      0.87      0.39       799\n",
      "          5       0.08      0.70      0.14      2875\n",
      "          6       0.30      0.59      0.40      5199\n",
      "          7       0.38      0.87      0.53      6151\n",
      "\n",
      "avg / total       0.69      0.56      0.60    174304\n",
      "\n",
      "12:37:06.995245\n"
     ]
    }
   ],
   "source": [
    "# Logistic Regression - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "lgr_params = {'penalty':['l1', 'l2'],\n",
    "              'C':[0.01, 1, 100]}\n",
    "lgr_grid = GridSearchCV(LogisticRegression(), lgr_params, cv=4)\n",
    "lgr_grid.fit(X_train, Y_train)\n",
    "print('Best Score: ', lgr_grid.best_score_)\n",
    "print('Best Parameters: ', lgr_grid.best_params_)\n",
    "Y_pred_lgr = lgr_grid.predict(X_test)\n",
    "print(confusion_matrix(Y_test, Y_pred_lgr))\n",
    "print(classification_report(Y_test, Y_pred_lgr))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ran VERY slowly and didn't to very well.\n",
    "\n",
    "### K Nearest Neighbors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.99099375593\n",
      "Best Parameters:  {'n_neighbors': 2}\n",
      "[[61953  1132     2     0    44     5   238]\n",
      " [ 3380 81129   128     0   250    96    27]\n",
      " [    1   136 10358    60    22   192     0]\n",
      " [    0     1    97   673     0    36     0]\n",
      " [   35   260    23     0  2556    10     1]\n",
      " [    2   118   164    36    11  4905     0]\n",
      " [  169    33     0     0     1     0  6020]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.95      0.98      0.96     63374\n",
      "          2       0.98      0.95      0.97     85010\n",
      "          3       0.96      0.96      0.96     10769\n",
      "          4       0.88      0.83      0.85       807\n",
      "          5       0.89      0.89      0.89      2885\n",
      "          6       0.94      0.94      0.94      5236\n",
      "          7       0.96      0.97      0.96      6223\n",
      "\n",
      "avg / total       0.96      0.96      0.96    174304\n",
      "\n",
      "0:42:54.411543\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbors - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "knc_params = {'n_neighbors':[2, 4, 6, 8, 10]}\n",
    "knc_grid = GridSearchCV(KNeighborsClassifier(), knc_params, cv=4)\n",
    "knc_grid.fit(X_train, Y_train)\n",
    "print('Best Score: ', knc_grid.best_score_)\n",
    "print('Best Parameters: ', knc_grid.best_params_)\n",
    "Y_pred_knc = knc_grid.predict(X_test)\n",
    "print(confusion_matrix(Y_test, Y_pred_knc))\n",
    "print(classification_report(Y_test, Y_pred_knc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very fast and accurate!\n",
    "\n",
    "### Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.606801547797\n",
      "Best Parameters:  {'alpha': 0.01, 'binarize': 0}\n",
      "[[28358 20367    43     0  7343   399  6864]\n",
      " [18282 41559  2210   129 19502  2698   630]\n",
      " [    1    40  5116  1519   738  3355     0]\n",
      " [    0     0    67   718     0    22     0]\n",
      " [  314   527   238     0  1736    70     0]\n",
      " [   74   248  1130   366   557  2861     0]\n",
      " [  384   476    22     0   294     0  5047]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.60      0.45      0.51     63374\n",
      "          2       0.66      0.49      0.56     85010\n",
      "          3       0.58      0.48      0.52     10769\n",
      "          4       0.26      0.89      0.41       807\n",
      "          5       0.06      0.60      0.11      2885\n",
      "          6       0.30      0.55      0.39      5236\n",
      "          7       0.40      0.81      0.54      6223\n",
      "\n",
      "avg / total       0.60      0.49      0.53    174304\n",
      "\n",
      "0:02:38.578421\n"
     ]
    }
   ],
   "source": [
    "# Naive Bayes - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "bnb_params = {'alpha':[0.01, 1],\n",
    "              'binarize':[0, 5, 50]}\n",
    "bnb = GridSearchCV(BernoulliNB(), bnb_params, cv=4)\n",
    "bnb.fit(X_train, Y_train)\n",
    "print('Best Score: ', bnb.best_score_)\n",
    "print('Best Parameters: ', bnb.best_params_)\n",
    "\n",
    "#bnb = GaussianNB()\n",
    "#bnb.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_bnb = bnb.predict(X_test)\n",
    "print(confusion_matrix(Y_test, Y_pred_bnb))\n",
    "print(classification_report(Y_test, Y_pred_bnb))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very poor fit, luckily it ran quickly.\n",
    "\n",
    "### SVC\n",
    "\n",
    "After some time trying to run SVC( ) to no avail, I found this in the sklearn documentation: \n",
    "\n",
    "    The implementation is based on libsvm. The fit time complexity is more than quadratic with the number of samples which makes it hard to scale to dataset with more than a couple of 10000 samples.\n",
    "    \n",
    "We have over 1,000,000 samples, which is a clear explanation of why this model won't run. We may be able to get some results using LinearSVC, and using only a portion of the data set. This reduces the kernel model to a linear model, but standard SVC isn't feasible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.659823189151\n",
      "Best Parameters:  {'C': 1, 'dual': False}\n",
      "[[39242  9908    51     0  4666   608  8899]\n",
      " [21736 36318  1977   102 19687  3929  1261]\n",
      " [    1    83  5286  1615   456  3326     2]\n",
      " [    0     0    50   712     0    45     0]\n",
      " [  124   325   209     0  2098   126     3]\n",
      " [    1   123   928   536   488  3158     2]\n",
      " [  618    25    22     0    38     0  5520]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.64      0.62      0.63     63374\n",
      "          2       0.78      0.43      0.55     85010\n",
      "          3       0.62      0.49      0.55     10769\n",
      "          4       0.24      0.88      0.38       807\n",
      "          5       0.08      0.73      0.14      2885\n",
      "          6       0.28      0.60      0.38      5236\n",
      "          7       0.35      0.89      0.50      6223\n",
      "\n",
      "avg / total       0.67      0.53      0.56    174304\n",
      "\n",
      "0:33:42.834540\n"
     ]
    }
   ],
   "source": [
    "# SVC - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "svc_params = {'C':[1],\n",
    "              'dual':[False],\n",
    "              }\n",
    "svc_grid = GridSearchCV(LinearSVC(), svc_params, cv=4)\n",
    "svc_grid.fit(X_train, Y_train)\n",
    "print('Best Score: ', svc_grid.best_score_)\n",
    "print('Best Parameters: ', svc_grid.best_params_)\n",
    "Y_pred_svc = svc_grid.predict(X_test)\n",
    "print(confusion_matrix(Y_test, Y_pred_svc))\n",
    "print(classification_report(Y_test, Y_pred_svc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Did about as well as the linear model, and actually ran a lot faster than expected.\n",
    "\n",
    "### Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.98606161075\n",
      "Best Parameters:  {'max_depth': None, 'max_features': None, 'min_samples_split': 2}\n",
      "[[57859  5022    12     0    64    17   400]\n",
      " [ 3392 80729   268     1   365   216    39]\n",
      " [   10   337  9823    76    28   495     0]\n",
      " [    0     2    96   671     0    38     0]\n",
      " [   67   495    40     0  2273    10     0]\n",
      " [   14   265   511    32    16  4398     0]\n",
      " [  355    48     0     0     2     0  5818]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.94      0.91      0.93     63374\n",
      "          2       0.93      0.95      0.94     85010\n",
      "          3       0.91      0.91      0.91     10769\n",
      "          4       0.86      0.83      0.85       807\n",
      "          5       0.83      0.79      0.81      2885\n",
      "          6       0.85      0.84      0.84      5236\n",
      "          7       0.93      0.93      0.93      6223\n",
      "\n",
      "avg / total       0.93      0.93      0.93    174304\n",
      "\n",
      "0:24:00.710758\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree Classifier - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "dtc_params = {'max_depth':[8, 32, None],\n",
    "              'min_samples_split':[2, 8, 32],\n",
    "              'max_features':['sqrt', None]}\n",
    "dtc_grid = GridSearchCV(DecisionTreeClassifier(), dtc_params, cv=4)\n",
    "dtc_grid.fit(X_train, Y_train)\n",
    "print('Best Score: ', dtc_grid.best_score_)\n",
    "print('Best Parameters: ', dtc_grid.best_params_)\n",
    "Y_pred_dtc = dtc_grid.predict(X_test)\n",
    "print(confusion_matrix(Y_test, Y_pred_dtc))\n",
    "print(classification_report(Y_test, Y_pred_dtc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Quite accurate results and very fast to run.\n",
    "\n",
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.992048482857\n",
      "Best Parameters:  {'max_features': None, 'min_samples_split': 2, 'n_estimators': 64}\n",
      "[[60218  2854     1     0    43     7   251]\n",
      " [ 1837 82479   212     1   296   149    36]\n",
      " [    4    98 10258    73    22   314     0]\n",
      " [    0     0    97   684     0    26     0]\n",
      " [   42   291    45     0  2498     9     0]\n",
      " [    3    99   300    30    12  4792     0]\n",
      " [  178    28     0     0     1     0  6016]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.97      0.95      0.96     63374\n",
      "          2       0.96      0.97      0.97     85010\n",
      "          3       0.94      0.95      0.95     10769\n",
      "          4       0.87      0.85      0.86       807\n",
      "          5       0.87      0.87      0.87      2885\n",
      "          6       0.90      0.92      0.91      5236\n",
      "          7       0.95      0.97      0.96      6223\n",
      "\n",
      "avg / total       0.96      0.96      0.96    174304\n",
      "\n",
      "7:15:32.557243\n"
     ]
    }
   ],
   "source": [
    "# Random Forest - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "rfc_params = {'n_estimators':[8, 24, 64],\n",
    "              'min_samples_split':[2, 8],\n",
    "              'max_features':['sqrt', None]}\n",
    "rfc_grid = GridSearchCV(RandomForestClassifier(), rfc_params, cv=4)\n",
    "rfc_grid.fit(X_train, Y_train)\n",
    "print('Best Score: ', rfc_grid.best_score_)\n",
    "print('Best Parameters: ', rfc_grid.best_params_)\n",
    "Y_pred_rfc = rfc_grid.predict(X_test)\n",
    "print(confusion_matrix(Y_test, Y_pred_rfc))\n",
    "print(classification_report(Y_test, Y_pred_rfc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great results (although not better than KNN), but medium/slow runtime.\n",
    "\n",
    "### Gradient Boosting\n",
    "\n",
    "I tried running Gradient Boosting with the entire data set, and it took too long to tune. As a workaround, I've tuned the model with only 10% of the training data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.949828535201\n",
      "Best Parameters:  {'learning_rate': 0.3, 'max_depth': 6, 'n_estimators': 200}\n",
      "[[55062  6786    20     0   281    34  1191]\n",
      " [ 8009 72896  1002     5  1960   961   177]\n",
      " [    2    81 10081   100    39   466     0]\n",
      " [    0     0    37   750     0    20     0]\n",
      " [    5    84    33     0  2751    12     0]\n",
      " [    6    42   234    35    13  4906     0]\n",
      " [  101    12     0     0     1     0  6109]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.87      0.87      0.87     63374\n",
      "          2       0.91      0.86      0.88     85010\n",
      "          3       0.88      0.94      0.91     10769\n",
      "          4       0.84      0.93      0.88       807\n",
      "          5       0.55      0.95      0.69      2885\n",
      "          6       0.77      0.94      0.84      5236\n",
      "          7       0.82      0.98      0.89      6223\n",
      "\n",
      "avg / total       0.88      0.88      0.88    174304\n",
      "\n",
      "5:15:17.904513\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "gbc_params = {'n_estimators':[100, 200],\n",
    "              'max_depth':[3, 6],\n",
    "              'learning_rate':[0.1, 0.3]}\n",
    "gbc_grid = GridSearchCV(GradientBoostingClassifier(), gbc_params, cv=4)\n",
    "gbc_grid.fit(X_train.sample(frac=0.1, random_state=100), Y_train.sample(frac=0.1, random_state=100))\n",
    "print('Best Score: ', gbc_grid.best_score_)\n",
    "print('Best Parameters: ', gbc_grid.best_params_)\n",
    "Y_pred_gbc = gbc_grid.predict(X_test)\n",
    "print(confusion_matrix(Y_test, Y_pred_gbc))\n",
    "print(classification_report(Y_test, Y_pred_gbc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Very slow to run, so I ran it with only 10% of the training data. That fit is good, considering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Results\n",
    "\n",
    "A brief summary of our model results. The scores are taken on the test set and averaged over the 7 classes.\n",
    "\n",
    "#### Logistic Regression:\n",
    "\n",
    "    Ran very slowly (2+ hrs/run), did poorly. About as accurate as the linear model from the previous analysis.\n",
    "    Avg Precision: 0.69\n",
    "    Avg Recall: 0.56\n",
    "    Avg F1 Score: 0.60\n",
    "\n",
    "#### K Nearest Neighbors:\n",
    "\n",
    "    Ran quickly (8 min/run), did very well. One of the best models.\n",
    "    Avg Precision: 0.96\n",
    "    Avg Recall: 0.96\n",
    "    Avg F1 Score: 0.96\n",
    "    \n",
    "#### Naive Bayes:\n",
    "      \n",
    "    Ran very quickly (40 sec/run), but did poorly. GaussianNB did worse than BernoulliNB.\n",
    "    Avg Precision: 0.60\n",
    "    Avg Recall: 0.49\n",
    "    Avg F1 Score: 0.53    \n",
    "\n",
    "#### Linear SVC:\n",
    "\n",
    "    SVC ran too slowly to be feasible. \n",
    "    Linear SVC was faster but only did as well as logistic regression (30 min/run).\n",
    "    Avg Precision: 0.67\n",
    "    Avg Recall: 0.53\n",
    "    Avg F1 Score: 0.56\n",
    "    \n",
    "#### Decision Tree:\n",
    "\n",
    "    Ran very quickly (2 min/run), did quite well.\n",
    "    Avg Precision: 0.93\n",
    "    Avg Recall: 0.93\n",
    "    Avg F1 Score: 0.93\n",
    "    \n",
    "#### Random Forest: \n",
    "\n",
    "    Medium runtime (35 min/run), did very well. One of the best models.\n",
    "    Avg Precision: 0.96\n",
    "    Avg Recall: 0.96\n",
    "    Avg F1 Score: 0.96\n",
    "    \n",
    "#### Gradient Boosting:\n",
    "\n",
    "    Ran too slowly to be feasible (1 hr/run with 10% of the training set). Did pretty well with that. \n",
    "    Avg Precision: 0.88\n",
    "    Avg Recall: 0.88\n",
    "    Avg F1 Score: 0.88\n",
    "\n",
    "Let's look in depth at each model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression Analysis\n",
    "\n",
    "Logistic Regression did poorly and worked slowly -- why?\n",
    "\n",
    "Digging into the documentation, it appears that part of the issue may have been using a single-class solver algorithm on a multi-class problem. By default, linear regression uses a 'one-versus-all' technique for multi-class problems, which means running the regression seven times for each pass. It seems a better solver choice would have been 'sag' or 'saga'. Let's see how it works."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/maxcalabro/anaconda3/lib/python3.6/site-packages/sklearn/linear_model/sag.py:326: ConvergenceWarning: The max_iter was reached which means the coef_ did not converge\n",
      "  \"the coef_ did not converge\", ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix: \n",
      "\n",
      " [[27873 11258   255    31  8130   939 14888]\n",
      " [17360 34573  3298   163 19690  6592  3334]\n",
      " [    0   438  3334  2014  1520  3462     1]\n",
      " [    0     0    56   666     7    77     1]\n",
      " [  184   510   145     2  1751   283    10]\n",
      " [    0   224   676   674   323  3337     2]\n",
      " [  353    71     9     0    60     0  5730]] \n",
      "\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.61      0.44      0.51     63374\n",
      "          2       0.73      0.41      0.52     85010\n",
      "          3       0.43      0.31      0.36     10769\n",
      "          4       0.19      0.83      0.31       807\n",
      "          5       0.06      0.61      0.10      2885\n",
      "          6       0.23      0.64      0.33      5236\n",
      "          7       0.24      0.92      0.38      6223\n",
      "\n",
      "avg / total       0.62      0.44      0.49    174304\n",
      "\n",
      "2:25:41.478254\n"
     ]
    }
   ],
   "source": [
    "# Linear Regression with a multi-class solver\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "lgr = LogisticRegression(solver='sag')\n",
    "lgr.fit(X_train, Y_train)\n",
    "\n",
    "Y_pred_lgr = lgr.predict(X_test)\n",
    "\n",
    "print('Confusion Matrix: \\n\\n', confusion_matrix(Y_test, Y_pred_lgr), '\\n')\n",
    "print('Classification Report: \\n\\n', classification_report(Y_test, Y_pred_lgr))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Well, it's not any better, it's actually noticeably worse. The model coefficients didn't converge in the maximum 1,000 iterations, which wasn't an issue with the default solver. The same was true for 'sag' and 'saga'.\n",
    "\n",
    "Looking at these results alongside those from LinearSVC( ), it appears that our linear models all top out around f1_score of 0.60. This may be the best a linear model can do -- the data itself may not be conducive to a linear fit. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### K Nearest Neighbors Analysis\n",
    "\n",
    "KNN did fantastic and was very fast to run. It worked best with the fewest number of neighbors (n_neighbors=2), which suggests the potential for overfitting (it did perform > 0.99 on cross validation), as well as sharp boundaries between classes. However, by doing so it was able to perform well, even on the rare classes. It did slightly better on precision than recall in the least common class (0.88 vs. 0.83), but is still on par with random forest.\n",
    "\n",
    "Let's run KNN a couple more times with feature selection to see if we can reduce overfitting and perhaps perform a bit better on the test set. First with Select K Best."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.988839634678\n",
      "Best Parameters:  {'n_neighbors': 2}\n",
      "\n",
      "Confusion Matrix: \n",
      "\n",
      " [[61723  1322     1     0    42     2   284]\n",
      " [ 4108 80253   193     1   324   104    27]\n",
      " [    3   208 10202    75    21   260     0]\n",
      " [    0     1   100   672     0    34     0]\n",
      " [   39   293    27     0  2515    10     1]\n",
      " [    0   139   243    32    11  4811     0]\n",
      " [  200    45     0     0     1     0  5977]]\n",
      "\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.93      0.97      0.95     63374\n",
      "          2       0.98      0.94      0.96     85010\n",
      "          3       0.95      0.95      0.95     10769\n",
      "          4       0.86      0.83      0.85       807\n",
      "          5       0.86      0.87      0.87      2885\n",
      "          6       0.92      0.92      0.92      5236\n",
      "          7       0.95      0.96      0.96      6223\n",
      "\n",
      "avg / total       0.95      0.95      0.95    174304\n",
      "\n",
      "0:14:41.010562\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbors with Select K Best feature selection using gridsearch to find the best n_neighbors.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "knc_params = {'n_neighbors':[2, 3, 4, 6]}\n",
    "knc_grid = GridSearchCV(KNeighborsClassifier(), knc_params, cv=4)\n",
    "knc_grid.fit(X_train_skb, Y_train)\n",
    "\n",
    "print('Best Score: ', knc_grid.best_score_)\n",
    "print('Best Parameters: ', knc_grid.best_params_)\n",
    "Y_pred_knc = knc_grid.predict(X_test_skb)\n",
    "\n",
    "print('\\nConfusion Matrix: \\n\\n', confusion_matrix(Y_test, Y_pred_knc))\n",
    "print('\\nClassification Report: \\n\\n', classification_report(Y_test, Y_pred_knc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We're still doing well with F1 score with our Select K Best features, but we're also still overfitting. Here's a new idea:\n",
    "\n",
    "#### A Problem with our 'n_neighbors' Parameter\n",
    "\n",
    "Because we're resampling all but the most common class, the function of this parameter is skewed. In the rarest class, there are 100 duplicates of every point. This means that n_neighbors is actually *not* finding the average between points from multiple classes when it is close to one of these points -- rather, it is getting absorbed into the rare class regardless of the value of n_neighbors (for small n_neighbors). Let's try the model with a data set that hasn't been resampled, which will allow us to actually use multiple neighbors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sanity check on array shapes:\n",
      " (406708, 55) (406708,) (174304, 55) (174304,)\n"
     ]
    }
   ],
   "source": [
    "# Recreate our X and Y arrays for processing with data_train_nosamp.\n",
    "\n",
    "X_train_nosamp = data_train_nosamp.loc[:, ~data_train_nosamp.columns.isin(['Cover'])]\n",
    "Y_train_nosamp = data_train_nosamp.Cover\n",
    "X_test_nosamp = data_test_nosamp.loc[:, ~data_train_nosamp.columns.isin(['Cover'])]\n",
    "Y_test_nosamp = data_test_nosamp.Cover\n",
    "\n",
    "print('Sanity check on array shapes:\\n', \n",
    "      X_train_nosamp.shape, Y_train_nosamp.shape, X_test_nosamp.shape, Y_test_nosamp.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.961832567837\n",
      "Best Parameters:  {'n_neighbors': 3}\n",
      "\n",
      "Confusion Matrix: \n",
      "\n",
      " [[61476  1896     2     0    39     5   178]\n",
      " [ 1859 82640   151     0   182    96    29]\n",
      " [    1   128 10329    54    14   153     0]\n",
      " [    0     2   116   658     0    40     0]\n",
      " [   38   263    24     0  2558     6     0]\n",
      " [    6   122   201    26     7  4868     0]\n",
      " [  180    36     0     0     3     0  5918]]\n",
      "\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.97      0.97      0.97     63596\n",
      "          2       0.97      0.97      0.97     84957\n",
      "          3       0.95      0.97      0.96     10679\n",
      "          4       0.89      0.81      0.85       816\n",
      "          5       0.91      0.89      0.90      2889\n",
      "          6       0.94      0.93      0.94      5230\n",
      "          7       0.97      0.96      0.97      6137\n",
      "\n",
      "avg / total       0.97      0.97      0.97    174304\n",
      "\n",
      "0:14:20.584780\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbors with unsampled data using gridsearch to find the best n_neighbors.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "knc_params = {'n_neighbors':[2, 3, 4, 6, 8]}\n",
    "knc_grid = GridSearchCV(KNeighborsClassifier(), knc_params, cv=4)\n",
    "knc_grid.fit(X_train_nosamp, Y_train_nosamp)\n",
    "\n",
    "print('Best Score: ', knc_grid.best_score_)\n",
    "print('Best Parameters: ', knc_grid.best_params_)\n",
    "Y_pred_knc = knc_grid.predict(X_test_nosamp)\n",
    "\n",
    "print('\\nConfusion Matrix: \\n\\n', confusion_matrix(Y_test_nosamp, Y_pred_knc))\n",
    "print('\\nClassification Report: \\n\\n', classification_report(Y_test_nosamp, Y_pred_knc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This works fantastic -- we have essentially eliminated our overfitting while increasing the f1-score on the test set. It turns out resampling isn't necessarily the best approach for every model! **This is our best result so far.**\n",
    "\n",
    "Let's use one more feature selection technique on this unsampled data. It could save researches a lot of time if they didn't have to determine 'Soil_Type' for every plot of land we're testing. We'll create a feature set that includes everything except the 40 columns of soil data and see if it's still adequate for modeling. It would also help to generalize the model with we removed 'Wild_Area' from the analysis. So let's take that out and try our analysis with *only* the numeric data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(406708, 11) (174304, 11)\n"
     ]
    }
   ],
   "source": [
    "# Grab all the columns except soil type and wilderness area for our new training set.\n",
    "\n",
    "X_train_nosoil = X_train_nosamp.loc[:, ['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology',\n",
    "       'Vertical_Distance_To_Hydrology', 'Horizontal_Distance_To_Roadways',\n",
    "       'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm',\n",
    "       'Horizontal_Distance_To_Fire_Points', 'Aspect_Cos']]\n",
    "X_test_nosoil = X_test_nosamp.loc[:, ['Elevation', 'Aspect', 'Slope', 'Horizontal_Distance_To_Hydrology',\n",
    "       'Vertical_Distance_To_Hydrology', 'Horizontal_Distance_To_Roadways',\n",
    "       'Hillshade_9am', 'Hillshade_Noon', 'Hillshade_3pm',\n",
    "       'Horizontal_Distance_To_Fire_Points', 'Aspect_Cos']]\n",
    "\n",
    "print(X_train_nosoil.shape, X_test_nosoil.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(406708, 11) (174304, 11)\n",
      "Cross Val Score: \n",
      "\n",
      " [ 0.96163416  0.96267592  0.96178093  0.96118023]\n",
      "\n",
      "Confusion Matrix: \n",
      "\n",
      " [[61476  1896     2     0    39     5   178]\n",
      " [ 1862 82637   151     0   182    96    29]\n",
      " [    1   128 10329    54    14   153     0]\n",
      " [    0     2   116   658     0    40     0]\n",
      " [   38   263    24     0  2558     6     0]\n",
      " [    6   122   201    26     7  4868     0]\n",
      " [  180    36     0     0     3     0  5918]]\n",
      "\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.97      0.97      0.97     63596\n",
      "          2       0.97      0.97      0.97     84957\n",
      "          3       0.95      0.97      0.96     10679\n",
      "          4       0.89      0.81      0.85       816\n",
      "          5       0.91      0.89      0.90      2889\n",
      "          6       0.94      0.93      0.94      5230\n",
      "          7       0.97      0.96      0.97      6137\n",
      "\n",
      "avg / total       0.97      0.97      0.97    174304\n",
      "\n",
      "0:00:17.566315\n"
     ]
    }
   ],
   "source": [
    "# K Nearest Neighbors with \"No Soil\" feature selection, sticking with the unsampled set. \n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "knc = KNeighborsClassifier(n_neighbors=3)\n",
    "knc.fit(X_train_nosoil, Y_train_nosamp)\n",
    "Y_pred_knc = knc.predict(X_test_nosoil)\n",
    "\n",
    "print('Cross Val Score: \\n\\n', cross_val_score(knc, X_train_nosoil, Y_train_nosamp, cv=4))\n",
    "print('\\nConfusion Matrix: \\n\\n', confusion_matrix(Y_test_nosamp, Y_pred_knc))\n",
    "print('\\nClassification Report: \\n\\n', classification_report(Y_test_nosamp, Y_pred_knc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These results are identical to those with the binary data included. KNN is apparently not using that information at all. This could potentially save a huge amount of time in data collection. **Everything we've used in this analysis can be calculated from a map and a digital elevation model (DEM) using simple GIS tools.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Naive Bayes Analysis\n",
    "\n",
    "Naive Bayes did the worst of our models on this data set. This makes sense, as BernoulliNB( ) is designed for boolean/binary features, not numeric ones. I ran GaussianNB( ) as well, and the results were worse. Not much to say about this one -- it's the wrong model for the job."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SVC Analysis\n",
    "\n",
    "First passes with SVC( ) proved impossible. After over twelve hours on a single pass, I canceled the run and decided to pursue other options. This makes sense given the notes in the documentation. Fit time complexity is more than the square of the number of examples -- with 1.3 million examples, it's not feasible (they suggest no more than a few 10,000's examples).\n",
    "\n",
    "LinearSVC( ) with a fraction of the examples proved more fruitful, however, the results were not good. Average F1 score topped out at 0.56. These results are on par with our logistic regression (which makes sense since this model is linear), and nowhere close to our KNN, decision tree, or random forest. \n",
    "\n",
    "It seems that the correlation between the features and outcome class is not linear, as all the linear models did poorly. Perhaps SVC with a kernel method could do well, but at cost of greatly increased complexity. Considering the long processing time and poor performance, this is the wrong classifier for this data set."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Decision Tree Analysis\n",
    "\n",
    "Decision tree did well and ran quickly. The best model used a deep tree with no limit on the number of features to look at before deciding on each split.\n",
    "\n",
    "Let's see how we do with the decision tree using feature selection, and if we can make our model slightly more resistant to overfitting. We'll use the 'No Soil' feature set since that has real-world implications. We'll also keep using our unsampled training set, as decision tree (and random forest) should be insensitive to resampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.899510213716\n",
      "Best Parameters:  {'max_depth': 32, 'max_features': None, 'min_samples_split': 2}\n",
      "[[57989  5026    12     0    80    18   471]\n",
      " [ 4947 78625   486     6   507   305    81]\n",
      " [   10   456  9524   124    31   534     0]\n",
      " [    0     8   114   637     0    57     0]\n",
      " [   75   553    29     0  2215    17     0]\n",
      " [   15   296   593    63     9  4254     0]\n",
      " [  476    59     0     0     1     0  5601]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          1       0.91      0.91      0.91     63596\n",
      "          2       0.92      0.93      0.93     84957\n",
      "          3       0.89      0.89      0.89     10679\n",
      "          4       0.77      0.78      0.77       816\n",
      "          5       0.78      0.77      0.77      2889\n",
      "          6       0.82      0.81      0.82      5230\n",
      "          7       0.91      0.91      0.91      6137\n",
      "\n",
      "avg / total       0.91      0.91      0.91    174304\n",
      "\n",
      "0:03:18.384062\n"
     ]
    }
   ],
   "source": [
    "# Decision Tree Classifier - find best parameters with gridsearch and display results.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "dtc_params = {'max_depth':[32, None],\n",
    "              'min_samples_split':[2, 8, 32],\n",
    "              'max_features':['sqrt', None]}\n",
    "dtc_grid = GridSearchCV(DecisionTreeClassifier(), dtc_params, cv=4)\n",
    "dtc_grid.fit(X_train_nosoil, Y_train_nosamp)\n",
    "print('Best Score: ', dtc_grid.best_score_)\n",
    "print('Best Parameters: ', dtc_grid.best_params_)\n",
    "\n",
    "Y_pred_dtc = dtc_grid.predict(X_test_nosoil)\n",
    "print(confusion_matrix(Y_test_nosamp, Y_pred_dtc))\n",
    "print(classification_report(Y_test_nosamp, Y_pred_dtc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This worked almost as well as it did originally with all the features (F1 Score of 0.91 vs 0.93), and we've eliminated overfitting by using the unsampled data set. Fantastic!\n",
    "\n",
    "#### On Overfitting\n",
    "\n",
    "So why was our Decision Tree (and Random Forest, as we'll see in the next section) overfitting? Let's think again about what resampling is doing: it makes copies of rare data points to be sure they are included in the analysis. But these copies all have identical properties -- exact copies of every feature. So when our decision tree learns to classify a certain point in the training set, it gets an artificial boost in accuracy if that point is from a rare class. In a way this is the whole point of resampling. It keeps our less common classes from being disappearing or being discounted in the analysis. With so few points in some of these classes (for instance, Class 4 has 2,000 points in the training set, while Class 2 has 200,000), the model is highly incentivized to classify every one of those 2,000 points correctly in the training set. Random Forest and Decision Tree are specific enough algorithms to be able to make that a reality, even if it means creating entire branches of the tree that are dedicated solely to classifying those examples correctly. While we do want our model to pay attention to rare points, this level of specificity is too much. Because Random Forest is simply a collection of Decision Trees, this overfitting propagates through the entire ensemble model.\n",
    "\n",
    "#### Visualizing the Tree\n",
    "\n",
    "Let's visualize the top few branches of our tree to try to get a sense of what choices are most important. We'll use the feature set with Soil Type removed from the analysis since it worked well with KNN."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import tree\n",
    "from IPython.display import Image\n",
    "import pydotplus\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/0uRZI22g7Tdf9gs1GC209Tw3cPnyFXVzgv1uOhlI77X8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\nnzbWoYNH7PpmjClO3KktE8fOtFkeBwztGqnBr2b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b5fBbKl93vItf7LlIXa6FQCzvFWR6VzwrVdmtZSwyolXe8RXQ1ZQQABBBBAAAEEEEAAAQQQQCAC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HVKVKFRuCdYVhy5cvX9CYGC8CCCCAAAIIIIAAApkS4NPnTHGxMQIIIIAA\nAggggAACBVcgKSlJy5cvtxVdTdDVPEzVCh8fHzVp0sRWdH3wwQftc9WqVQsuFCNHAAEEEEAAAQQQ\nQAABBBBAAAEEEEAgHYFGjRrJPB544AElJibaOymZqrAmDDt48GD75fKGDRu6w7BdunRRcHBwOi2y\nCgEEEEAAAQQQQACBgifgddqZCt6wGTECCCCAAAIIIIAAAghkJBAdHa05c+bYkKsJus6bN08xMTEq\nVqyYQkNDbUXXDh06qE2bNgoKCsqoOdYjgAACCFwkgVtuuUUbN250H/348eOKiopS7dq15eXl5V4+\naNAgDR061P2aGQQQQAABBBBAIKcFNm3apCFDhig+Pt596D179sh8lFWxYkX3MnNnkY8++kgmGMaE\nAAII5DcB8zvbzJkz3ZVhly1bZr983rp1axuGDQsLs+/L+fn55behMx4EEEAAAQQQQAABBDIjMJHw\na2a42BYBBBBAAAEEEEAAgXwsYIJRroqukZGRWr16tU6dOqVatWq5g67t27e3Hy4mD0vlYxKGhgAC\nCOQLARMKMf+mZzQ9+eSTevXVVzPajPUIIIAAAggggEC2CZjfS80XdDyZlixZombNmnmyKdsggAAC\neVrgwIEDmjp1qq0Ka6rDbt68WYGBgerYsaN69OhhA7Hm30Nvb+88PU46jwACCCCAAAIIIIBAJgUI\nv2YSjM0RQAABBBBAAAEEEMgXAnFxcVq0aJENu5qgqwm97tu3T/7+/mrZsqVMRVcTdDWPsmXL5osx\nMwgEEECgoAq89dZbeuqpp+ytNNMzWL58uRo3bpzeJqxDAAEEEEAAAQSyXaBp06Yy1yXpTTVq1LDh\nr/S2YR0CCCCQXwW2bt3qrgo7ZcoU7d+/X6VKlVK3bt3cYVjzZXYmBBBAAAEEEEAAAQTyuQDh13x+\nghkeAggggAACCCCAAAJWYO/eve6qriboaoKvJgBbrlw5d8jVBF5btGhhA7CwIYAAAgjkH4Ht27er\nWrVq6Q6oTp06WrduXbrbsBIBBBBAAAEEEMgJgXfffVePPvqokpKSUj2cr6+vhg0bpmeffTbV9SxE\nAAEECpLA6dOntWLFCncYdsaMGYqJibG/A4aFhdkwbPfu3e17gAXJhbEigAACCCCAAAIIFAgBwq8F\n4jQzSAQQQAABBBBAAIECJXDq1Cl7e2tXRVfzvGnTJnvrM3Pra1dFVxN2DQkJKVA2DBYBBBAoqAKh\noaGaN2+ezAejZ0+FChXSCy+8YKvDnr2O1wgggAACCCCAQE4LREVFqXLlyqlet7j6sn79etWuXdv1\nkmcEEEAAgX8EEhIS7O9+4eHhMlVhze+BZlmjRo3cVWG7dOmiokWLYoYAAggggAACCCCAQF4XIPya\n188g/UcAAQQQQAABBBBAwFRzMG9km4quJug6d+5cRUdHKygoSG3btpUJuZrAa7t27VSsWDHAEEAA\nAQQKoMCnn36qe++9N80Kaps3b5a5fTATAggggAACCCCQGwQ6depkf8c1X+48e2rSpImWLVt29mJe\nI4AAAgikImDeNzTVYE0Q1gRiTZVYHx8ftWnTxh2GNe8Z+vn5pbI3ixBAAAEEEEAAAQQQyNUChF9z\n9emhcwgggAACCCCAAAIIpCKwbds2d9DVBF6XL19uw0zmltauoKsJu5oPBM2b2UwIIIAAAggcOHDA\n3uby7ACJl5eXWrRooYULF4KEAAIIIIAAAgjkGoHPP/9cd9xxh86+djG/47711lt68MEHc01f6QgC\nCCCQlwT27dunqVOnusOwW7duVZEiRWS+dNCjRw+FhYWpadOmMr8rMiGAAAIIIIAAAgggkMsFCL/m\n8hNE9xBAAAEEEEAAAQQKuIC5LdnSpUttRVcTdDWPXbt2ydfXV82bN7cVXU3Q1YReK1asWMC1GD4C\nCCCAQHoCPXv2tB9wJg+RmADJu+++a6vCprcv6xBAAAEEEEAAgZwUOHz4sMqUKXNO1XoTxtq5cye/\n/+bkyeBYCCCQrwXMXUBMRVhTGdaEYs0XJ0uXLq3u3bu7w7A1a9Y8L4Mvv/xSkyZN0gcffGC/jHle\njbATAggggAACCCCAAAJpCxB+TduGNQgggAACCCCAAAII5LzAoUOHbMDVhFwjIyO1YMECxcbGqlSO\nc+QBAABAAElEQVSpUgoNDbVhVxN0bd26tQICAnK+gxwRAQQQQCDPCowaNUo33XSTTp8+7R6DCZDs\n3r2bDyLdIswggAACCCCAQG4RuPzyyzVx4kR3ANZct5jfh2fOnJlbukg/EEAAgXwlYH5XXLZsmTsM\na/69PX78uKpXr+4OwprKsObLCZ5MLVu21OLFixUcHCxT0fvaa6/1ZDe2QQABBBBAAAEEEEDAUwHC\nr55KsR0CCCCAAAIIIIAAAlktYN5QXrdunQ27mqCrCbya12aqW7euO+hqKrua19xuLKvPAO0hgAAC\nBUsgJibGfpkiPj7eDtzb21tdunSx1X0KlgSjRQABBBBAAIG8IPDjjz/q+uuvd39xx1y7/O9//9OQ\nIUPyQvfpIwIIIJDnBczvjnPnzrVVYU112Pnz59svJDRu3Ngdhu3cubOCgoLOGevRo0dVvHhx+2+4\neU/TvA961VVXafjw4fb30nN2YAECCCCAAAIIIIAAApkXIPyaeTP2QAABBBBAAAEEEEDg/ARMBVdT\nydUVdJ0zZ44OHjxoK7iaSq6mgo0JuppHyZIlz+8g7IUAAggggEA6AldffbXGjRunxMREmQDJF198\noVtuuSWdPViFAAIIIIAAAghcHIETJ07Y343j4uJsB3x8fLR//36VKFHi4nSIoyKAAAIFXODYsWOa\nPn26Owy7cuVK+fr6qm3btu4wrJk3y37//Xf17ds3hVihQoVsFdgvv/zynHUpNuQFAggggAACCCCA\nAAKeCRB+9cyJrRBAAAEEEEAAAQQQyLxAVFSUO+hqqrouWbJECQkJqlSpkjvkagKvzZo1s28KZ/4I\n7IEAAggggEDmBEzwtV+/fnYn88HjgQMHVKxYscw1wtYIIIAAAggggEAOCQwcOFCjR4+2FQMvvfRS\n/fnnnzl0ZA6DAAIIIJCRwN69e20QdsqUKfZ527ZttgqsqQZrgrKmaqx5LzT55KoCe8MNN+ijjz6y\n1WGTr2ceAQQQQAABBBBAAIFMCBB+zQQWmyKAAAIIIIAAAgggkKZAUlKSli9fLhNyNQ9T3dW84Wsq\n0zRp0sSGXV2VXatVq5ZmO6xAAAEEEEAgOwVM5bRSpUrp+PHjttLO2LFjs/NwtI0AAggggAACCFyQ\nwPjx43X55ZfLhKW+//57DRgw4ILaY2cEEEAAgewT2Lhxo7sq7B9//CFX5e7Ujmi+jGnufDVy5EiZ\nLzcwIYAAAggggAACCCBwHgKEX88DjV0QQCCfCUyYMEExMTH5bFQMBwEEEMi7AuYDrS5duqhMmTK5\nehDR0dG2eoEr6Dpv3jz7/4mpnteuXTu5gq7mVl9BQUG5eix0DoGCIBAbGytz3WeC6kwIFHSBTz/9\nVBEREXrooYfs/1kF3YPxI2Bun92jRw8gEEAgAwGupzIAYnW2CCQmJuq2226TeTa3yfb398+W49Ao\nAqkJeHt7q2fPnipatGhqq1mGAAJpCOzevVsVK1ZMY+2/i83fsVOnTunWW2/Ve++9p3379mnx4sX/\nbsAcAggggECeFuD9ljx9+ug8AnlFgPBrXjlT9BMBBLJHYMuWLapZs2b2NE6rCCCAAALnLTBs2DC9\n9NJL571/duy4adMmd0VXE3hdtWqVfXM2JCTEHXRt3769GjZsKPPGLRMCCOQugR9//FHmlqlMCCCA\nAAIIpCZw+PBhbrmaGgzLEEgmwPVUMgxmEUCgwAh89tlnGjJkSIEZLwNFICsEvvvuO9144406ffq0\nR82ZKrBly5a1n9fNmjXLo33YCAEEEEAgbwjwfkveOE/0EoE8LDCxUB7uPF1HAAEELljAVfnr/2Z/\nqBpNQi64PRpAAAEEELhwgcc63HfRKzOaW3ItWrTIhl1N0NU89u7dayvMtGzZUpdccoleeOEFG3o1\nb8wyIYBA7hcw132+vj5asHdY7u8sPUQAAQQQyDGB+TO26L/9vrno1585NmAOhMAFCJy5niqkQ1NH\nXEAr7IoAAgjkHYFql9/LNULeOV30NBcJ/P333/Lx8bFVuz3plgnJRkVF2Uenti0V/stXnuzGNggg\ngAACuVggInKeLh04hGupXHyO6BoC+UWA8Gt+OZOMAwEEEEAAAQQQKGAC5k3R4cOH68knn9Rvv/2m\nrl27nreAuaWWK+QaGRlpg68mAGuCraaa6yOPPGKfTfCVWyyeNzM7IoAAAggggAACCCCAAAIIIIAA\nAggggEA+FwgPD7d3zDIBWFcRGteQzR2zgoODVapUKZUpU0YVKlSw78GWLl1a48aOVamSxV2b8owA\nAggggAACCCCAQIYChF8zJGIDBBBAAAEEEEAAgdwmsHLlSg0ePNiGVL28vDRp0iSPw6+nTp3S6tWr\nbdjVBF1N6HXjxo0yb7w2aNDAVnMdOnSoDbvWqlUrtw2d/iCAAAIIIIAAAggggAACCCCAAAIIIIAA\nArlW4LnnntOhQ4dkAq3mYUKurvkSJUrIvJ+b2rRi+XIV9klKbRXLEEAAAQQQQAABBBBIVYDwa6os\nLEQAAQQQQAABBBDIjQKxsbF66aWX9Oabb9o3SU31V/OYPn16mt2NiYnR/Pnz5Qq6zpkzR9HR0QoK\nClLbtm01cOBAG3QNDQ1VsWLF0myHFQgggAACCCCAAAIIIIAAAggggAACCCCAAALpCwwZMiT9DViL\nAAIIIIAAAggggEAWCRB+zSJImkEAAQQQQAABBBDIXoG///5bt99+u3bt2nXO7bIWLVqkhIQE+fr6\navv27e6gqwm8LncqBpjba1WrVs2GXF9++WVb3bVJkyYyt95iQgABBBBAAAEEEEAAAQQQQAABBBBA\nAAEEEEAAAQQQQAABBBBAAIG8JUD4NW+dL3qLAAIIIIAAAggUOIH9+/frgQce0Pfffy9vb2+dOnXq\nHIP4+HhddtllWrt2rQ3HmhBss2bN1LlzZz355JM29FqpUqVz9mMBAggggAACCCCAAAIIIIAAAggg\ngAACCCCAAAIIIIAAAggggAACCOQ9AcKvee+c0WMEEEAAAQQQQKDACHz55Zc2+BobG2vHnFrw1aww\nFVx3796tu+++2wZd27Rpo4CAgALjxEARQAABBBBAAAEEEEAAAQQQQAABBBBAAAEEEEAAAQQQQAAB\nBBAoSAKEXwvS2WasCCBwwQKrZq3QoaiDKdqp1qiGqjaoptiYWC38a16KdeZF+6s6XfBttfds2a3R\nb/yogc/cqFKVSp9zjNy4IDEhUatnrdTCifPUtHsLtbyktUfdjDl8TEv+XnTOtoHFiqh42eKqEFJJ\ngcGB56w3C/KiU6oD8XDhunlrtG/b3gy39vX3Vbu+HTLczpMNcvrvwM6127Vo0gJVd/6eNQ1r4UkX\n091m/459WjRxgTYv2aC7Pnkg3W3z+8rDew9p17qdatS5SapDNVZr56x2r0tKSlJAUIDaXtHevSy1\nmb1b99i/w34BfmrZs7WKOX9vz2c6ePCgOnbsqNmzZ+v06dMZNmG2qVevnq3ymuHGbIAAAgggkO8E\n9u85poWztmY4rkrVSqhJ68pKSEjS4tnbNGPSerXrGqJOPWtnuG92b7Bz62F9/n8zdNeT3VSuUnB2\nHy7H2186b4fmRGxSIV9va9645bkV2Rc552TpvO0qHOCr1p1qqE7Dcmn288DeGG3ZcECtO1ZPsc3J\n2ARFjF+bYpnrRUARP3W9rK7rZarPu3dGa+nc7e51SUmnFBjkr+6967mXMYMAAggggEBWCBw4ckwr\nNm5Xt1YNs6K5XN1GXHyClm/YrhWbdmhb1H5VLldK9apXUKsGIfp9+kJd1zP99xpy9eCSdc6Mc9bS\ndc5Ytym0SR21aRhi72CTbJNUZzPaLzYuXn/OWJzqvoEB/urdsbl73YJVm5w+rHXej/ZW3y6tVK1C\nGfc6ZhBAAIH8JHD4yFFNnj4rxZC8vLx0bZ/L7LK5i5Zp285dKdbXq1VTTRte+O927332jQoX9tMd\nNw1I0X5ufZGQkKCZ8xbprykzFNYpVJd17+RRV1MzNjsGFy2qcqVLqVaNqs58UKptbd62U699MFzP\nPXK3Klcon+o2+Wlhaj9vqY3Pz89PV17WI7VVmV729/TZOnTkSIr9TPu1nfMSUr2qAgoXTrEuO1/E\nHD+habPna/aCJXr1qQez81B5vu0j0Uf11U+/aceu3c7fxc7q3rFtihxB7MmT+n3S1FTHGegUfLmi\nZzf3uozacm1o/u4fi4lxvdSOqD2665aBMu2lN53vfum1yToEEEAgOwQIv2aHKm0igEC+FTABvFUz\nV+inV0bZMT72wzCVr1nBzptQWLWG1fXO4De0Y/U2NejQSHd+fH+KC9bzhdmydJMiRv2t9v075pnw\n6/ZVWzV7zAz9/dVEVa1fzeOhFykepCr1q+rN61/W3i171PLSNnbcW5ZtUtT6nVrw11zVaVNf1z11\ng/Oc8k2KvOjkMUwqG/7x0W82nNjp2q4qWaGUdm3Yqb+/nKCGnRrbgKIJEpvg6O5NUVkWfs3JvwN7\nNu/WZGc84z8Zp7s/vfCgqgmomzDn6Dd+kHkTrKBO0fujNfbdXzTxsz/VY/ClaYZfv33mS0WOnpGC\n6f1Fw1O8PvvFb+/84gRfF+qOD+6VOc4zlz1u582/h5mZDu87rBEjRtjQqyfBV9O2qQg7Y0bK/mbm\nmGyLAAIIIJC3BUqVDZIJtt474HtFH47V1YNbqlnbqnZQp5zwYvShWE36baWq1Cxpw68bV+/T5LGr\n9OvIxQqpVzZXDH7Nst0a9/1S/advg3wXfn3jiQn648dlCgr2156dR/XxKxG6/7keGnz/v1/Qeu3R\nv3TyZIKeeKOXs020HrrpJ117W2sNHNImxfk5dOC4vno/Uj9/sUD9b2pxTvg1/PfVGnbn2BT7uF50\nvqROhuHX957/W5PGrHLtYp9/m3t3ite8QAABBBBAICsEvv5jun6dMk9zvn4pK5rLtW0sXL1ZQ14e\nrmJBgbqxd2f1coKaW6P26cMfJ2nC7KUKCiicL8Kv+w8fVbehL+qRG6+w43zv+7/0f9/+qZ9fvz/d\nAKwn+42NWKD/vvJ5quf4svbN3OHXJz78Xvud9wNfvOMaHTtxUs98+rN9b+WbF+8u0O+FpQrHQgQQ\nyPMCxYsVVd2QGhpwx8Pasn2nLunaUe+//JR7XA3qhMgEEh9/+f/ssi/ffUW1a3r+WZW7oVRmvv75\nNwUFBuaZ8OvKtRs0+s/J+uL70TIunk7GuH7tEF039EGZMGuvsM66qndPLV25Rus3b9Wff09TuxZN\nNezBO9TWeU4+LVm5Wt/8Mk5XXd6zQIRfPxjxrWYvXKIBfXupQrky1mfEd6PVuV0r9b00TIePRGvC\n1JnauHV7loVfmzasq+f/72N7XiuWK6un7h+qw9G79csfE/XbX+G6vv/lev+lpxRUJPWCQsnP14XO\nT5o2S0++8o79nIbwa9qah5yfgw5XDFS7ls0UtWefPvn6B7Vs0lCRf3zv3mnM+L9164NPu18nn+nd\no4s7/OpJW2bftRu36MrB9yRvRtdccWmGwdfz3S/FgXiBAAII5JAA4dccguYwCCCQPwRMMDPspp42\n/GrChmdXQKzqhF+bdm9uw6+terVVxVrnVjM6H4nQKzvqq60/KLh0sfPZPUv2MUG2zUs3qPl/WnnU\nXs1mtXTpf6+w4VePdvhnIxNKrN64php1amLDr10GdleHqzq7mzi0+6C+eOR/eq7XE3rgy8fUts+/\nlSHOxymz43J3JBfMJMQl6Pk/X1XlemeCHYsmzrfh1+pNaqr3XX1tD/s/cp0e6ZDyl5oL6XpO/h0w\nwfKet15mw6/ehXwupNt2XxNQN0Hh2b85bzAsXH/B7eXVBvZv36suA8P0+wdj0hzCPmebJKci3v/W\nfO3extfPV8XLlXC/PnvGhF6/e+5rvTnrA1WsXdk++tx7pd4Y+JLemfNxpoL7Jqjs4+PjVOVLcB/G\n29tbhQoVsm+eJCYmupcnnzlw4IC2bt2q6tWrJ1/MPAIIIIBAARDw9vayoVZT1XXm5A3q2a+h2jiV\nQ5NPfW9optce+8suqt+0gq67vY0NvybfJifnTRj0igH/fjhkQq8RGx5ViVLZ/6FETo5zyh9rnMCH\nl6Zvesw+z5+xRY8O/kUfvTzFBn0rVy8hs82YbxdrytqHFRDoqxp1Suvhl3rqnuu+V/0mFZwgcxV3\nl6O2H7Fu3348x70s+cxUp+rrZ+NuUqPmleTr9+815H/7faMefeon3/Sc+agdR5SYcEoTlv/7xSs/\npw0TrmZCAAEEEEAgKwVMZfERY6dq175DmrF4jTq3SP//qKw89vm09f3ESF1/aYdM7/rz33NsaLN/\ntzb69Mnb5O+8t2Cmto1q2cDrC5+N1jvfjc90u7ltB/OF3Bue/lANa1bWLVd0sd17Yeg1anzdo3re\nGeOLd1ybapc93e/PmYs1/v3H1aJeDfn5/vux3hUPvKm+Xc+8V2tCxh//PFlrRr+tSmVL2uOZ45o+\nmJ+xLi0bpNoHFiKAAAJ5VcB8ltSsUX11CW1tw6/9LgtTjaqV3cMxFUlvHdjfhl9N+G/glb3T/TKC\ne0cPZiJ//y7L2vLgcOdssv/gIS1esdoGfs9ZmcqC5o0b6M6bB9iQZCqr01xkjJs0qOsEOFvb8OsN\n/a/Q1Vdc4t7ehPceeu519bzudo388HX1c0KersmEZHctna7SJdP+TMG1res5s+Ny7ZcbnuPi4zXh\n+8+dsHBN2x1TMdOEX02l4XtuvcEue+zu29W2V+rXBOczhrJO9d0bnICrCTU3aVBHQwZd427GLLvr\niRd17FiMfv78Pffy7Jox5/vX8ZO1ePnq7DpEvmh39B+TnKDrDypZ/Mzn/a+8P1wvvv2xrZjbvvWZ\nSv6m6uukH0eoVdNGznXfmWtnM/hLrx+SIjjtSVtmv/c//0aTf/rC/e+j+XtdxoO/l+e7nzkmEwII\nIJDTAt45fUCOhwACCOR1gYCiZz6MDghO/UPpQNf6f56zarwXM/hqbnf+3q1vaN+2vZkajo8rsOhc\nSGd2SsvXhI5N6NWE69664RXN/HlaiqYz43S+40pxwIv4on77hu7ga1rd8PX3Vfcbe6a1+ryW5+Tf\nAS8n8Ggmb6+su2SxP5eZ/5E8L6us2sn83Vs3b02WNFerZR1Vqvvvm4CpNfrnR2OdoHtLFStTXGWq\nlLWP9IKvpo0xb/+sGk1DVNN5uKbOA7rrpBNkDR85ybXIo+eKNSvqkUceUYxzG5Y1a9Zo8uTJ+vzz\nz/XUU0/plltuUVhYmEJCQhSQyi1ZFixY4NEx2AgBBBBAIH8KFCnqn+bAgosH6LYH/721n7kVrJnO\n41I1zWN4umL+zC368KUp52yem4Kvu5yQ6bL5O87pY2YXLFuwQw85QVbjbd5gb9ulpi7p30hJSae1\navGZW1D+8tVCVaxaXOYcuaZGLc98kfCLd2e6FtnnRi0qqUbt0imWuV4kxCfp1gc62uBzYJCfDb+a\nAOzRI7Fa6Ryr62V1XZum+jzqk7nqEFZLJUsXUYXKxeyD4GuqVCxEAAEEELhAgT9nLVbHZmf+X/r4\n58z9znyBh8707iY4+fzwXzK9n6lo+vC73yo4MEDvPXKzO/iavKGnbu2nSmVKKi7+3y+/Jl+fE/Pb\ndu/XvJUbL+hQkcvWac6KDU7wtau7HXPtc8NlHTX813Adj41zL08+48l+8QmJemhQbxuQDgosbMOv\nJgB75NhxLVyz2VbSNW3uPnDYNr12a5T7EP5+Z4KycU4bTAgggEB+FTAhVzMFB537pUXXOhN+NcUV\nsmoq4lR9zclbyifvt/lc66Z7n9C2Hf/+e598fVrzptiEmczv5ZmdXI5n71exfFmN/OB11QmprgFD\nH9JP48584di1XWaCr+c7LtexLvazCS66gq9p9cXf3083XdsvrdXntbxoUJFU9zNhb3POJ0+frbi4\n+FS3yeqF5nM87/P4+crqfmRHe1t37LKVpC+k7Xjnevc/Xdq7g6+mrUFXXWGbDC565jyabR69+zZ1\nbd/GVuz1c744Zh6Ho49qwdIVuvw/3ez2nrRlNtyz74BWrFmvkOpVVLVSBfuoUrG8ChdO+/3TC9nP\ndo4/EEAAgYsg8O9XRC/CwTkkAgggUNAENi/dqNWRKxXnvOFpKqM2C2uR4hdNc5v6Wb9MdyqmXq7F\nkxdo28qt6nNff3k5lZJWzVwhU7nSBNfMtG3lFm1y2jt7Mr+4dh7Qzf5SY9ZldMwDO/dr7rhI9bqz\nj3au2a754+eqdJUy6nxdN/tmgKku+t6tb2p5xFIbhDPtt+7dTiXKO29MO+NYNXO5c4xN8nbe0DVV\nWktVTP2D6LP7eSGvTaDzzo/u0+NdHtDUbyfbap6mPVMt4Wyn/2fvKuCiyt7oWZNQFEFRUbHB7u7u\ndnXtjjXXXLtdxVq7EztRLAwMFFREEJVSEJGQ7rD3f787vmFmGIaBBXf/7v34vXn33fvdOu8+5s19\n551LS6ZT3Jtnr3kbTSuWQA2G+9/pV3qYSX0j9Uqniw8Q/CoQpApcs20d6BdQ/iHqdssVr5y9kY+p\nCpPCbX4jAyl7uvuev/VN14ccekztw/2S45Pw5NpjBHkHwKhEYT7+jNk+u43GTkRQOK+G1EMb9mgC\nOofU7wCvt7zv9bs2kjfD/f5zfs7IR06kVJiPSes6IVLr3+ljetcKNZDO6d3jtxAREIZiTNm5AhFJ\nLUry6y0+kv34vPqI94OuE7OqZXj73ye+5+OA1EqrNq+OIqVM5H1NL/DONxhn155kddph2MrRMG+Q\n/UowhK8dI6tSu/dM384VroesGMkJsGm1Ny4iFp4O7mg5sI2SSx6dPCAFX8dz9ug/T/Z2s5JDOgf6\n+vqwsLDgW1qusbGxCAgI4Nu7d+84MTYtXxEvEBAICAQEAv9dBOie0PbsC3TqWy1dEMLexcPRzgeh\nwXFccZTImmRJCR9x1uoJiGBJSqZN21ZA+cpFkBD3ARdPPEVy0ie06VYJZuWM8D75E5zvv4Hns3ec\n9NmlX3WYFJfd5xHx9bdBJzjplkifRYrlR4uO5uxe9i84O7yBnn4eEMFT0Tzd3sHlgT8vl5RQG7Uu\nJ/8d8fnzVzxmZcrUb0vC3tYbb3wi0ZERTM3KGykWo1X47eso7NtwD5dOumHasvaoUT9FdVWrAlSc\nhk9pwjFQjG7eviJO73dG/m9kV79XEcirozxFVbCQHifEPn34VjGrxjARXVWxowx2lzxRp7GZErlW\ntSAiyFofcUFy4ieuENyqiwWmLW3HCbCqvuJYICAQEAgIBAQCfxeBfedvY8+CMQiLisNVRzf4Boai\nXInU8wV0D3P/qReevXqLnIy0U9GsGFrXq8qrJ9XYy/ddMaZXa+5z89ELFC9siKFdm0OXkSsU7baz\nOx67+6Ige7Dep00DGBVQJgglJL0HqYu+evsOVcqVRJv6VVEgnx5XDO0/ZxO/b9l34TaKGRdE5yYy\nVSrF8tWFLQ/ZMIJmEmYP7QYD/ZQXXBR9c7MVXiynDMRX1k/J4pOScf3BM3j7B3MF0zasvyVMlO9p\niFhry3ALj4lDmeJFUNPcjO+lMrTZE+brDl/E8WuOWDmhP1ej1SafOh8bexceXaWc8svGlcuaIun9\nR1x/6IZeTP1W1bTNV6eS7H5UMf+Fu0/QpIY5DL+RJeic6evmxYp951C7UhkUMsjH+0Ztal7LQjGr\nCAsEBAICAYEAQyA+IRG2bBl6L5/XKMHIYG2bNwaRwiSLZt8xJ22uYPzQX2B7+x4jkL3CtLFD+epk\nYRGRIGXP4f17cfcXXi/VKl7ScwJael4inqZXJz1DuOP4mP++b1inBi7fuAvv12/Qr3tHVCxbmpMY\nh02Zg1v3H6KwUSE+L9C1XUsUM5E953nJfJ1cnnGyWyNGxlRUYpX6ldV7InTusFzMlnIfiIMnz6N/\nj868CnpeZ//QmRP4SMGSjO5rKM7N3YtjYl6uDMO9kcZ+Jb9/j7sPHsP1uSfPM6hPV5gWTblnCggO\nwfmrNzFxxEB4vvKFzbXbnOCnTun3iZs77j16gvcfPqBT62ZclZU37NuH3b2HcHJ9BsOCBnxZeCPD\ngorJGsMzxo/QmC4lTh83XAqyPnngvpMLkpLfo1a1SmjHxiCNmaywhMQk9tLxF3xi5dF8kappU3d6\n4ykqJhbnLt+Af2AQalevgr/Yn9R+Om+7rE7hExvTOmyM0Pnw9vGDo/NT3pR87N5wcJ/uIPLuuSs3\n4PsmAB1bNUW1SrJn4GnVTWM/kJ1zMhp7PTu25Xsihnq+ZPe6BQzQvUNrnk7X6VV2jYdFRKGsWQnU\nqlqZ73milh8+b97CcsseHD13CavnTwddl5k1IrEqqlNTOURM7dymOapayPpNPtL1olgPjfFmDerw\nsUnx2pRFftsPHuOk2XIN2qN0SVPMnzoOQ37uIT9P5KPOMptPXVkiTiAgEBAIfA8EcnyPSkQdAgGB\ngEBAIAAcmLMb1htOo27nBkxRsS4OL9iHxZ3ngAhzZLeP3sSYikOxb9ZOXNlpgyOLDrLtAB5feYT1\nQ1dhSZe58HV9JYeSSKqhr9+hhHlJRrArj+S4JGwb/yc8GHFQ+iGdXp1U9qymk3Hg9924st0GNlus\n8dLJC1vGrOdtpco+sglaUoAkI9VVUlwlQhuRACdVH83CedFrxs/48vkL5reZyQmx3DmbP0hhMhdT\nOSA1TKo7gBF31eF0bKkVQl4Ho+uknqjYwALHllnxlmW2X9pgRhUEMoLpBnbeiADZj5H+nC49wIRq\nIxHi947X/4m9vbd94iZ+/ut2qo/n9m6YXHss7wd3yOKPN89fY17bmRyzjmO7ITEmAVPrjMOdYzez\nuKbUxZkz3G02neXjs0I9c058Ja8Kdc1xnl0TNIYlO7rkECeXEum7Sd/mOLX6GE+SfjCndZ3Q+f87\nfUzvWqFGECl0VtMpKFXZDH1/HwBnRnSd1mAC5raajv2/7+LEZZpEoOvwOSP8SsRdHX0d9hDnKzwY\nCZtUVLUxGj+bRq/FlFpj2Vh6i3lnlvAxHPUuEp6O7pq3B+7aVJGmz5dPXzBw8TCOPym/Opy1x5Ta\n4zghP61MoW9C+MQVkeJVjcoI8X3H01XTsuK4QIECqFq1Kjp16oSRI0eiUKHUbciKekQZAgGBgEBA\nIPD/jcCD26/x8O7rdDtBxNSdlndgUb0oylQ0xm+DT+CPmZd5PlISrdWwFLauuIVH9n6c+EoJ+Qzy\nsnusnAgNiuPEVyLJdq+7BXl1c3MVUiKnDu+4nxNXyZ/UTStWMWET1blQmimYmpgWgK9XOGaPPI2x\nPazg+VR2v0i+ZOvmX8OBTfc5QZZUSf9cfANjuh9CTFQSVzSdP/4cfu1zBBeOPsWy32zg9jgQJ/c9\nxqhuBxEbnSwrRItPv5cRmD/uHHrW34rXL8Ox+fhADP61IYgM7MoIqOltaVVBKqqqFhIUi/wFdFC9\nrowgosOweusbhfi490quJcsYsrgPSIxXr5am5Kzh4MYFD05M1uCCz5++YvKCNkyVtgoKFdbHdWt3\n9GqwFfdvpPwG05RfpAkEBAICAYGAQEBbBDxeB8LQQB8mRgUxrk9b/nt5x5kbarMv23OWEWPDMLFf\nB9SvWh50THbyuiMaDl+A+dtOYNp6Kxy3dcQL3wDM3HgEnSav4kQD8iPV0EmW+xEZE49OTWrinit7\nIWTQHHj6ydTXyYdIpsMWb0dVRnqdO6InLt57gur9Z8EvOIyRZfVQtXxJrtpasVRRlCii/W9uZw9f\nKh7VKpTi+7Q+ujWvIyfrPvd5i7a/rmTEopyM1NsGsQlJqDtkHo7ZOsizk+Jp71kbGJm0Hqb+0gk2\n9s546u0vT08vQP0dvXwXajMcvN+8w+nVv3F8STnV8dlLjdsDlq7OfANkJIyi7JwqmjEjz5C9eitL\nV0yjcGbzUd7zdx6jR8u6FOSmx+ZoF47ujSeefmgxZimWs7Hi7huIy5vmcNKJ5Cf2AgGBgEBAIAA8\n8/BGi16MyMqeMY0f9gti4uJRo3UPHDljw+E5fPoCytRvi+mLLRl57DgWrN7Eto144fUKVqfOo1Kz\nLlhouUkOpc3123jtHwgLtuR9rWqVEceItWNmLuJES+l5XXp1Etl2+NR56DJ4HA6xOsbPXoqHLm6M\nQHgC7fqNBBENibTZvmUTXq8pU10lxVXdbwqOm/cexkS2zP0gpiT56/ABmL1sLct7Ut7G7AzUrGKB\n3AzLh0/cQAReD0ZCHDRhFjr8MlqJFLx47RZGcnyLKaOHoGHtGqBjsrT6RQTOys26cpXd2UwN8/OX\nz2jJzhsRK8ku3biDhp37Y+bSNdi6/yg27rbi5NWR0+Zj7fb93Ef6WLJ2K67csse4of042bBR1wE8\nH6WTiub42UsQGR2NLm1b4C4jIFdr2Z33Q8qf1ftZ7Pys3bGf1dcSHdg5nbtyA9r3H8XaEPO3qyLi\n8arNu3k5pCyaO3dupTK1qTu98eTt64eug8cz0mYFLJ4xEZFRMYx4fEtOqiRl5BLFTPh1k8hebCIV\n4Cb1a7Nr6CWPa1KvNie+UsMa1K7Orr0LqGJenrdTU91EPt2w+xC/vurVrM6Jr5SpXs1qWMfwpGuQ\nLIYppXYfNhF9urTH9HHDGEHaDq4vPHiaNh9ejKg7fOpcPg4obH1gCx+3wSFhcGCEZU2b42PXdKsg\nIviZi9c4Flv+WJCuPxGEe3Vqp9ZPU1lNGWGWCNekTBz4LpTj1nnQOE6MVlvYt8jM5tNUpkgTCAgE\nBALZiYCyrEZ21iTKFggIBAQCPxgCkUwxdXX/Zal6FfQyIFUcEQxJSXGXl5Vc+XPm4XmYzIhtRJqb\nuncWWg1qC7dbLrh38g5XT93wcBsnUBIxsBhTTnx4PmWSlyowMSuKpv1asDf2cuADU2dYM2A5ChU3\nwvDVY3n92tRZjxFx2wztwImupExKBFGymU0m8/r6zOzP2yupzZqytpByJZn9iduIDonixEX68U5l\nnVh+GAEe/nJ1Wu6YTR+k8klLt5M6Lil2EpGy35yBSjjRDf+NA1dBWJOVr12Rq9ZSmBRYM9MvbTCj\ntyn/HG7JFHy7oHS1MlQdV1+lcxjIlE6LlimGKzts2Hk2QtOfW/D0kZbjMNZ8KA4ykvTCCyt4XFZ9\nENF2w7DVaNy7GVddpXJJDdbPzRc7Jm5GuVoVUbKS5gcR6tqi7TWQly3JNmjpCKzutxQv7rrx/lN5\nNH5KMiIpEarJSO2YyLCHAk+BCKO0tRvREV4PUn6QpnWdmJQpihkNJ2aqj9pcK9S+CxvP4jNTQq7c\nRPaW8s+MAEvKvs37tZJfOy0HtsWlbRe4wjORsmmckr1kJO2uk3rJf/jzSDUfb93f4MzaE3A8ew9E\nFJ53bilqMcVgyRzO2OPg3D3Sodo91Xkq5qLaNG0iCxQpiC4TevCN+nBixRFYrz/FSL0bsdllF/SZ\nSrGqxYTJJmTy6OZRTWLEn7yMTPKZE70NjAukShcRAgGBgEBAICAQyA4E1i+4zkimOrzouJj3eOUe\nim4DamisikirS6fY4Mz9X6HL1FctmMKq4y1fnGIKpV3710D1eiW4qiipuN644M6JmvkNZHV4PA1m\nE8jNefm3r3ohPCQeZRl5lpa7bdGxIrb/cRs+nmE8v0W1ojA00sO7wFjUa1pa3qZxs1vgpo2n/JgC\nF0+4cTVS2+fTINW17mA/9GAE1bVzbbFyV28s29oT18658zp3nBvCyCI50KBFGUwdeAJuTgFo3qGi\nUpmqBz4eYdizwZ6TPasxMurWkwPRmJFsJbtm/QKEpyajOp3DFmpyUUojYun431tw4jAl1G9eBv5M\nrdbF0Z+TfCVnUtWl86ifX/NybJK/un1UeCIn7q7e00ddsjyOCK8DxzXgGxGWd6y6jf0b72Px5Auw\nfjQJBoysK0wgIBAQCAgEBAJZgcDOs+wFeEbsJOvUuAZKMlXTI1fuceIiqa1KRvNaB2zu4PDyiTyq\ntkUZuepq//aNcePRc0aCfcAJtJXKmHKfFXvPgRRXrS7fw6gerbDz7A2uBtu3bUOevnryQFj0mY65\nW4/j/PqZ7MH3V4xYspOrxxLJlYwIpRfuOMPLL5gTZo0L5kdAaCSa1dJ+NRpqu9ebYF5e6WLarTpE\nRN3hi3egd+v66NFCRuqcwtpCxFYi8NYyLw3qJ/U5H5tryMfmm8gWj+nLVW35gYYPIh2vsbqIc7ec\nUK9yWZxdMx1tG6SsCnDWzonjoqEI5GJzoNF39qVyCWNKtPRCdB5G/FE0PSYgQBYSGasYLQ9nNh8p\n3xIR98Di8fKyKEAkaVpNYB4jRa8/ehmbZw1PpfKrlEEcCAQEAgKBHwiB9bsO4MSFK+n2iIiOgyfO\nQt+uHRiZrC33nzZ2GFcWHf/7Eq5gScqIN+89wInzV1CckUydr51hCrF+sChfBjWrVsLlm3eZgmUK\nwa1MyRJM8bQTf16XlJyMn8f8xtVJ1y6axcvXps7KFcth7/rlOH3RFu9Cw3Hl6C6uMtuqSQP0GTUF\nD5hiJhEzJVVIc9aWFo3qyfu78xAjybZowp9BkMJjjcrmTJ32LiN79pf7ZFcgF1NyJxVXIge7vvDk\nRMT5v43jip5SnXRvsPfoGRzfuZ5H1alRhS3h3pKHCxjkV9uv49aX8S4snOFelov+dG3bEkvXbYO7\ntw/3p/wjfunFia5EwiRSLVkDRoi1ZkqZv08azY8pTIRiv8cyMZjqDJtuLK/DY5ly+zamjklk4n7d\nO3H/tYtngZQyiUB86chOHpeVH0SyPnDiHHwfXgf1nYxwIcLtjCWWOLhpVYarexsUgg27DoLImXcc\nnbgy8LHt65gKaiulsrStO73xNGraAj7+JCXUUQP7cPKpYmW9OrflBFhSZZWMFHLpuqJxQueBzMn1\nOVPuHcSvHzrWVLeeri5W/D6VXxN3HB/JlVzpmqliXoErJFMZx9jYyaeny5WH6XjZ7MmcGE1hTebu\n/YoRh/fgzKVrqF+rGi4c2ob27LqSjK7P2cvXSYdq93Q9JL6WjS11DolJSYx4vRY0vonIXbtdH369\nS9e2ah5SsKWxarXFUjUJ6ZVFbZfaTwR8+t9H6rnrdx4EEcrTsszmS6s8ES8QEAgIBLIbAeVf4tld\nmyhfICAQEAj8QAgUNDHECMuxqXp0adt5tsx9kFL8pa3nYVqxpJz4SolE+CtiZsJJpGM2TISegR4K\nFTXi+ep1lU1IS4qYtPy7qjX/JeUHy9Glh0DKiwusl8nr0LbOPGyymIyIpJIREfLpzSfSoXyvuNoG\nEW/L1iwHwoFUVN2ZqiVZsE+QnFQqz5hNgfcJsrc7iVxJpooTqYWaMpzXD1uFX7dMQf2ujTjpU7U5\nGe1Xepi5XHPmKqR1OtaXV1W2ZnkcCT2L3GzJCrKLTGW3XO0K2D1tm9yneAVTxDN10ay2pzeeIOhl\nICrWt1Aqumbb2rh36g7srK5h+KoxSmnaHGTkGiB1WyJPk7pwm2Ed+ASM/cnbILKoZOfWnULZWuX5\ntSDFla8j+/ErKb9SvLrr5DFTQs5sH7W9Vki1NzYili11/ImfRzNGbM6rlxcRQeFSc/m+57S+nPz8\nwPo+JzcT8fMdU2mWiNBKzt8O/J754ozlCTy84ADzBpWw4Pxy1GxTO5Vr51+7o/1o2bJBqRKzIYKI\ntIOWDIMhu85JlfqF/TM06N44VU1EVOameDF98/rKHqTlYsp2+oapSbOpChIRAgGBgEBAICAQyCIE\nZqxoj/rNZC8hUZG3Lnvhrq23xtKvnn2OD+8/c2VVyTEyLAElShvirV8UJ79SfP/R9Tgp9fKpZ/hl\ndH2uSprIiLPFS8qUvjr1qYZKjDhrVCQfL++Jgz8v7u3rKE5+lcpW/dokJVhVO7LjIcowdViJ+Erp\nZuXZagylCuLy6eeYu7aLnEBagqmkEgmVrKy5jGRCBNu0zOt5CPass4fdRU/UqF8C204PRuPW5VK5\nDxjbAD+PSFEVS+WQwYjbV7xgXDQfBo2X/eah7OMZ8fcBIxov++0iU19N4qqwj5hS7yuPUNSoJyPi\nZLAaubvdJfZAhZF66Xxoa4Tj5IVtYGySD5ZzbPGYKQK36ao94UfbeoSfQEAgIBAQCPz3ECDV0mev\n/DkpkXpPL7WP7tUai3eyl4Ev3QWRPSWjuZAKpYph6KLt2DJ7BLo2q42pA1LS9ZnSG5ExJeIr5Zs+\nuAvWHbkEh6fenPy65cQ11LYojekbrKRiUaFkUUTHJfLjaw/dQGqrHRkJV7KajGQacn2XEpHzJylR\nyz21PSfrG9kXpj6mjRGZ9+Xbd6hXRfl+hAiqp28+ZIRee6yaNAAVzYrhPvVv2S6snjwApYsXRjFj\n2X2YunoI7zWHLuLCXWc0YOq51utmoE39qqlcxzMV3lE9U+ZbUzloiCAyrjqT+m5ipP5l4Mzms7F/\ngvoMpyKFlMsltV7q5yZGev1jvzUmrt6PwNAozBspEzxQ10YRJxAQCAgEfhQEhvTtwZeyV+3PRabK\nqmjX7tyHt+8b1Gdqk4rWvkVjnGTk2YMnz2HNwlkoblKEJ3dvL/tuIOKrZHnyyF5ukI5pSXfJFq3Z\nAr+3gbCx2i4nNmpbp843FdeyZiU58ZXKrFRB9r0YEPxOqoLvFZ+ZUMSN0/uhz4iBZKS8GsBUHuPj\nE/jx9/ggdU8yqQ2qGFF7SamWFGG3r17El6YnRUpVU+wXEYqJbGxS2Ajv33+A/UNn7u7j5y8ny+ow\nhVEyIgNLVqlCWdy46ygdYjVbtr4TW1pe0U7s2iBXv9y0xwp1qlfBlPkr5S4Vy5bmarvyiCwMbN53\nhJOFJeIrFU31EWmZCJGbV8yHQX7t5zAovw574aZ86VKw3LqXq+8SUZII1aqmbd2axtNth0cgQuuC\naePlxdN5I0LzM/eUuTcSThoxoA/DfzcioqK5+mv+fLLVgQ6etMawfrL7k1MXrmL3umXysjTVTU5E\nAqfrkc7biF968+eNRHwn1WPJiIx979ETDJsyB+sWz0aZUiXk17Tko7h3c/fiarlElCZC70WrHYxM\nnvpZ2MQRAzF2SD/FrBkO6+vpYYflYmxbtZArFv++Yj0mz1+BB5dOqC3rgu0tNKhVg18Hqg4ZKYvI\nxg+vnETVFt35/zpN5FfFejKbT7EMERYICAQEAtmNgGz2IbtrEeULBAQCAoEfEIGc7E1+k9JFU235\nVFQR6W1GWsJcJ5/sR6ciFJUayyZaJbXYn5hCABlNfGtr3kxR8sp2G7Qe0g612skeSmekTnX1UP2s\n2amN/XiRjHwKFDHEcab2enHLOfnS9X8xdYHvYYkxCZzwS7iWsEj7gfjoDb9Cjy3RZvnLcizpOheU\nL5VlQb8UMXvz/DUnRaqqXErEV2oDqZ62Hd4RY/+cKN+2uO7BGvuUpXJStTOTEQFMbZZMR195DErj\nL9ArIFMla3sNUOH0w7fnb30QxK4Fl2uPeX3Pbj9F7fYpRArCrRRTglU0xYkOKV7ddZLZPmbkWiHV\n44/JH+Dl6M6bkhidgM8fP7PlkGpJTeP7Rr2a8v8LNpvP8WPqLykGa7IjCw9w4mvhUkUwdOUotcRX\nyk9kVFJSTW/TVFdm0pr0ac7PYbBvkNrsxiVkBJsPiTJCuqJTckIyJ/tLyzsppomwQEAgIBAQCAgE\nvhcCLTuZo3Yj5fsM1bp9vcI52XHeui6Qti0nBuKSyxR0ZWqvklWtbYoqtYrjzEHZy2K2516gy8/V\npGR2L/8TJ1puY2qvh7c/QBmmAEtGKlyK9hNS7q0V46Uw3af4vQznKrRSnLSv3Uim2u/3KkKKUtrn\nYIqzZFRGWrZpKVudghFfi5csgGlL26slvlJeIoLq6OZOd0urHsV4f99InD/iiqVbeihGc7yO3xmL\n0TOaw/tFCOJiktFjUC18/PAF9ZqVVvLN6MGNCx5o271yRrNx/w69qrJ7IOCtb1Sm8otMAgGBgEBA\nICAQUEXgMFNkJRXQjpP+kG/Wt2XzJDvP3ORKrIp51k8bDAM2nzNg3mZ0/W0NYhOSFJNThfUYaca0\ncCFExMSDiLYhkTEY1q0FNkwfKt9cjq3G3T2Led7nPgEgdVJSd1U0VQVTdfMziv7qwhali/No38BQ\ndcmp4rzeyOYc9FWIpI2rV+S+3t+UZFvUrsRIwh1x6sYDVO8/G4Rp3m8vm6cqlEUsYsRiIoSWKmqM\nFRP6qyW+Ur5cbM5FN2+edDd1dZgWMeL3eh/YC9OKlsBW6yKTsFBMo3Bm89GYkdRxpTLpvq/r1DWY\n1L8DRnZviQcHljOF23JYdeA8XLz8JDexFwgIBAQCPywCRYwKcYIbkdwUN9UOe756zaPyMQKaotGS\n7GRer2T/M6XndNJe0Tet8MMnbpzMRqS+Di2byt20rVOeQSFAK8qQqf6+V51RMC1qwgiJLzBt0Sqm\nUvuaK2J+/Uu7F1AUqstUkJaYJ8JvPn09+bLz6gratHweDBj5kZRxOw4Yg5i41GIwiv0i7In4unTd\nVmxkREdSgCVTnV9RrYueRUh40UqNHi99YFZCdl8i+dK9DSl0UttJNZRIlJtXzpdvz+/YwPHScck9\ny/bULjo/hJWqSWPQ2zfj39s0/rt3aI0DG/9AQmIS+jK14OiYOKUqMlK3pvFECqJkpLSqaOrmuUYy\nXOl8HT17kbsSYXXyqMFwfOyKV4zE/No/EMZGhpBIseSkqW5Kp3M3nSnIkhrz1Vv3KAq37j1Ex1Yp\n11yrJvVBis6kMmvRtDNX/s3L7vPSsvmrNnK1YBonq+fPUEt8pbw0ZnQZ4Tq9La16FONpfJNaMSlQ\nP33hhQ8fPiomy8NnL18HqehqMm3LIuXcbozQ7+Mne26sqUzFtMzmUyxDhAUCAgGBQHYikFpWJDtr\nE2ULBAQCAoH/IAJ0E07LhPs8ecnfIlQkgBUrL/uxpW4ZcW2g+vThE7b9+icMixXC8NVj5Vmyq04q\nVzJSml3U8XeM+XMC6nZqwNRuA6Wk77L3cHjB6yH1Uk2TD2Wql8M6hy04vOgAbuy7iplNJuNPpx3I\nXyhlUj+r+0UE4A9JH7hKpjr1zp/YDxqyt+5+6ZIiuePf/MhnKOurt5MnKjeREa6pSCJaEpky33dS\n5GzWvxWOLzsMIoUWLmXCia5UP9mXz184Zq+cU94K5QnSR8rQk2KU9pntY0auFSIrhzAF112/bcPA\nRUPZ+XXDoKXD5aRzqUF0jXef0ht7pm+H+/3ncLS+h1FrUt6AlfwU9wsvrACN6dOrj2F+25mozgi1\nv8wfzFVgFf3o/4jbbVfFqFRhIrv0mvZzqvi/E2FQuADysWumeHnZMoqqZRH5VZ0KLvnFRcahbI3U\nbxirliGOBQICAYGAQEAgkJ0IECG1x8CavIqoiEQUMpYpTSjWmSPnT3jjE4lPn74gd27ZPYpiumKY\n1F8XTbwAN6cAONz0wdqDKd+9Qf7RGNXtIOYxVdbmHSrCn5WpzhRurdUl88l8g4K6cHcN5kQY6YEX\nOZcqZ8TzGBTUUZtXm8gdZwbD5YE/dq25i+Gd9qNhy7L4dU5LpgKr/GLZC5cgkAqrJqP78RFTU5aB\nU+cbF/seOy3vYMXOXsiTN/V0FKnbDhhTX56VVGCLFM+PIRMayeMyGoiOTMIThzdYtlWZbKttOYbG\neihgqMvVdrXNI/wEAgIBgYBAQCCQFgJfmQLqMVsHOB/+gy3DqvwdPnjBVk7QJEXPXq3qyYuoXsEM\n9/cv5cqw+y7cRpORi/HIagUKGeST+ygGiHwZGhWLtkzZVJovc/cNROcmyi/uSnmoTUlsRSd7F680\nSaHkqzh3JuVNb9+slgWc3H1x67E7+rVL//vc8FufnF74oEkN2UpAVEcptlIWKdwWzC+7f6N+rZz4\nC2/vjD8PY8LqfQhn5I7pg7qobdL59TPh4OaN1QcvoN2ElWhVtwrmj+rFVWAVMzzxfI3bzh6KUanC\nOdk95TQ19ZgzNVqywLAolCthIs8X+e0l/LTIr5nJR8Tm+0+9sHPuKHk9FKC4oPAotGsge2mrsKEB\njq2cjIq9p4HIsrUtyij5iwOBgEBAIPBfRaBQwQK86w9d3NC0QR05DER6I2JbwQIG8riMBIi4Nnbm\nQq4uuXbRLKWs2VGn6nfzkrVbYf/IGZeP7OTEPOsrN5XakJ0HpLBJRkulS/cf6uqrUcUCj66eAhEN\n9xw9jQad+8HlxjlI+FAexX4RobZdv5HYxJRQSe3z5es36orVGEeETyJfXr55B79PGp3KV2rvC69X\n6NquZar0rI6g/hmyMebs9iLVM+PyZWQvOmd2DFJbOzOF27mTx2IVU1sdPGkWbA5th/RcOiN1axpP\ncQmJHBYn12coWbyoEkSqc13FixZBV3bu9h07A1JIJqLxoc2rcfiMDQ4x9Vc6N6MH9lUqQ1PdkuOA\nnl1Afht3H0Jpdu2Syi1dv5LReV29YAbaNm+M3xb+wa7NRQiLiMKsCSMlF6X9JXbd3GfjeOWmXWjZ\neyjaNGuIRdMnchVYRUc6b3aMaKvJaP5u5q/q61GXr3XThrjj6AR15FxSzCXF4z3rl6vLmipOU1mS\nM6kkVyhrJh1qvc9sPq0rEI4CAYGAQOBvIJDjb+QVWQUCAgGBwH8SAeltwbQ6ry69Yj0LvGfqh35u\nvkrZXj/1AZHKTMoo/zhQctJwcOqPo3yp9wnbpkK/gGwCOD4qnhNRs7ZOGfOQli+X7OTKo6Dl3In4\nSpbem5ZSvqzYB/sEYfvEjShiZoLxmyanWSSRg+8ct4MuU34lhdV555ZyxdVHNg7f8mRPv8yqlObl\n3zt5W6lt8YwE+MjGEXoGerzttnsu4wNTElW0u8dvITwgTDFK67C6sUeZK9aTPTDwuC8jDEsFvnX3\n56TTivUtpCit9mnVI2VOK52Ub7tO6slJwVbz96HV4HZSFk7CLWFRCgGebxETGi2P1zbwd/qo7bVC\nRN2CJoaYuOM3mFUtgxGW49Bjah+1TWzFlJgNjA1A1yhNKOQ3Sn/CjIjJiy/+gT/s1vPJiHltZmBZ\n9/kgdWfJgl8F4YH1fY3bw/PS+JZy/f29J1O7/Ys9EKvUqIrawnLnzY02wzrgpZMX+1+Q8n8iKS4J\n79j12rh3M7X5RKRAQCAgEBAICAS+NwJO9n5ceVRdveZVi+J90iecOeCslEykzZP7HivFkSIokSLX\nzr+GClVM2Hd3yvTKjtV32H3yV058pUzq7pPp/uALm+BPz6rVMUVSwkd4PQtRcvVyewciZpYobagU\nn9EDUsPdZT0Uh2xHsvuxHBjWcT9+7XOYk3qlskitldRTNW12FzUTRZIZrhsX38DsVZ1AJFfJwkPi\n1ZKD7S554pyVC2Ys76BW+VbKn97+FiunUo1iKFpC9nAzPX/VdNeHb/n5q9VQ9gBKNV0cCwQEAgIB\ngYBAICMIXLrvirqVy6YivlIZ4/vK5ki2n74uL5KIrMcZWTa/ni5XbT27ZjpXcrW5KyOYyB0VAk7u\nPqB8HRvX5IqxZsWMsff8LSSrKEmduOaIgNBIVCkne+mFVFQVLTI2AUTEJaPZsy8Kv/V5pBYfM4Z0\nRVGjgpzw+9znbZo5/N+FI5qp1NZj2JA5uL1U8vV4HYTPTLWtQdXyPP7Qpbt87qF1vapw2L8MLepU\nxs4zN5TyqB4Qmfbin7Nxc8cCRqTNgba/rkCP6WvxiBFtJfMJCMH5O481bqQgq86GdW0OUst9+PyV\nUrKr9xtUK18KFUqqn/vNTL6L7LzUrGiGEiayl6GkConkTPedCd+Wnab4osYFUbdSWX6uJT+xFwgI\nBAQCPxoCaT2PoH6qS6tfqxqHgIhuiubu7cOXi6clzzNjKzbugLfvG+xYsxjScvZRMbGcsJmVddJ8\nApnidzORRInsOLBXV058pfTvpfpK6p3jZy9B6ZKm2MqWcU/LiBxM6p+k8EkKqxcObeNEyPNsmXky\ndf1a/ucOfPr8mRNfyUfd/ArFazIiRFowst8jl2dcZVTR97j1JtgLJAAAQABJREFUZfbycy7e9t2H\nTyH5vUyxXfI5du4S3ga9kw4ztFc39qQC6tesxtVZSe1T0Z4+90RhpuBalqkXa2vq6lk0YwInb960\nf4B5f/ypVJQ2dac3nqpaVOBlEmFTGxszuB+/NoZM+h2TRg2CDlupYEjf7pwAS6q8RIqWLL26Jb88\n7Hnj5NGDcffBY8xZuQGktqxoB06c4/eLbZs3gpPtKbRq0gDbDxxTdEkVJjL81WO7cdf6MLtfzIUW\nvYagy+BxIEVnyV699se5Kzc0bhklnhMGRO5WZxds7VCraqVUJGN1vhSnqSwpD5XZnam/ZtQymy+j\n9Qh/gYBAQCCQGQRSns5kJrfIIxAQCAgE/oMIJLLJX7LIoAj+Vp4qBET2Igv3T1nSa/CyEciVJxeI\n2CgZEcRePvLCEJYmvXVHaqFkRJJUNCJxkpGComREnD2/8QxaM5JdrXZ1pWg4nLXnhEpt60yOl7WX\nlm+XjOqhOqUfTYZFC/EkIuFR3Jvnfkyl8z0nKT5hS7rHRcTCds8l7hMVEonEb6oGSXGyt/+I+JtR\nC/uG30emPiEZqYM+vODACYG52A+bOScXKZEKVXGitl7fe0XeD1JhJUKiRETMbL/Sw6xulwYoU70s\n7hyzw84pW/Ds9lNc3GKNrUylt3YHmXpHj9/6Iio4Eos7z+Fk0NeMGH1ixWEQZoVLFpG6nKF9YowM\n76RY2TmVMpeuVhYtB7bhyqKKxFqvB+4oVq442o3sJLlqtc/MNSAV3J7VReTfOLbEX6nKym8WSmql\ne2eyCQ02/ugaofFM5vXAQ35dqLtOMtLHpNhErjIrjW9trxUiKz84fx9fmBrcZ/YgibCUxoLUP2mf\nly3R12lcd35um/7cUorWam/eoBIWWC+Dpf1G5GZLDxIJdnmPBXjr4Y/mv7TiSsakZpzWZnl3Y7r1\nJEbL/o99Uri+pEwXNp3Ftb2MmP1taT5+He27gvFbprDrJ4U48tb9DRZ1+h1eDz141m6Te/FrX5F8\n63D2Lup3a4SGPTQrwUl1i71AQCAgEBAICAT+LgLvAmJ4EfGMsKpqz58EYcEEa65wSmkJcTKfpETZ\n/WaHXlVgYmqA9Quv4+BmB7z2Dsc1a3csZwqkXfvJFLSkMvPq5EKvIbXgwVRZew+pLUXzPRE9I0IT\ncO/6K5DyqEScDX8XDyLSkhmb5EMk8wl8E40AvygkszZ8/HY/Hh2Vci83dXFb5M6TE5dOpky008Me\nt8cBoDQi3RI5luzTxy98Tx8xrF6yD8kp9/g8Io0PUnvddmoQjtqN4aqsnATb9wh8PMPQ5efqOHFn\nnMbtyM0xaZTM2sXunWYOP4WChfRge+4Fju9x4hspzs4fb43iZgWV8hLhdNPSm7Dc1xd0TtRZXIzs\n98XHD5r7R4TdNt0qqysCPh5hGN3tIJ4+CuDph7Y44vR+Z9D5I6N7oNOMCL1oYzcYGunxOPEhEBAI\nCAQEAgKBzCJA3yvrrC6iW/MUhTnFsprWNEfxwoacPPng2UueRHlI7ZX2ZG2YmqtxwfwwKpCi+kqk\nUK83wTydPs7fcQaV1alJTR43dUBnBIdHo8sUS9xz9YTbS3+s3GeNOLYcbklGnuzCFGGrVyjFCapT\n1x7EHWd3bD15DRNW7UWHhrL7HyJQhrJ5HL/gMLwOCkOiyovc8spVAkTa3btwLFOp1UfvmRuYMqm3\nkgeRdK1vO2HTcVvoMxIEkUQHdmwCB+ZHxFzJHBkepKY6ontLHuUbGMrVZOlAj+Xr1qw2wyQ/T0vv\ngwi059bNwN09i6HD5haJBNtzxjp4+gWhf/vGuL9vqcbtzu7FaqswYSTfcb3bYuOxlHnI94zkc9XR\nFdvnjFRSwfN4HYhOk1fzc52RfFLFpOLao0XKXLAU35qNDyLg2ti7SFH8XHn4BaJny9T+cicREAgI\nBAQC/+cIxMTF8x74B6V8H0pdiv2WFh4ZjcQk2e/k6pXNMZgR70itVJHY6PDYBeVLl5KrUEr+kdGy\neQapTNp//PgRsfEJnCxLx67PPbB+50FOwOvQsilFcTttY4vk5PfQtk5arp7s4yfZ71IKR0bJ6k9+\nL3t+WLSIMUVzQh7dIzz3fCnv2ymbq4hj7SJiL2207D2VGc+UOimeTKqDH2j54R8QxD0VyaGfGSnV\nmhFXuwwah7x58uDsvk0wMkz5fU8YkUVER/M9tXX3kVPy+5p2TJHTuJAhjNhGpq5fSeyFjpCwCL60\nPSlg7rI6wX2DQ8MQEyt7Xhr/rV8f2X2FZJHM9wOrX7qHWjDtV57Uvv9IHGGKo7a32Wp90xfwdFq+\nfvq44QgKCUX7/qM5mfLpC08sW7+NneN4lDItJhWbob009qS9YuYVc38DkTePnrsoj6bnYaRGvJKl\nSc+M5YkaAvLxH5gy/kn11GqLJUoUM8HGPVY4fPqCvARt6pbGflrjqWWjejAvV5qTme8xtWGy4JAw\ndk05I/BdKB+TND4kIwJqGUboff/hA5o1kN2TjB70Mz+3vTrLXgCTfNOrm8ayZGNYGQb584HGRmVz\n2UtSUpqP31vcvCd7uUtPVxfdO7RmYy1lfEp+6vZEgLex2g7Hi8ehkzcvJ8F2HTweHowgT+q1j66c\n1Lg5XFRPsqXrZ9Xm3XD3fiWvlv6/PHX3wrrFs+VxioGzl6+jV+e2ilE8rE1ZpJQ8Y4klaDxLRn1I\nZNfV3CljpSi+pzaRyvID56ecsK9tPqVCxIFAQCAgEPgHEci5hNk/WL+oWiAgEBAI/KMIREVFYcuW\nLWg/qjMMTWQET00Ncr35BCdXHME732BGfvuMEL93XMGTSJTJjOB5efsFXN9/lRPrKI3IYxaNKqNA\n4YKo2qw6zq0/hbC3oTzvuXWn0Lx/Sznx8Oaha7jGyHVUDvkQAdKouDFePvbi+QK9AhDLSKYUT4TF\nFb0WcfJpqcql8fyuG4iEevvIDVzddQk//z6AL2mfXp3u957DesNpTlh7n/ge5etWxJOrTrDdfZEr\n1bJXLbnSo25+XXg6vuDEU1qavenPLRhxsTQnddqxdge9DOTLwJOyqDPLX7hUEd7HkyuPcIxiw6Jh\nXLIwa7epJnh5GinXnl51jJMeCWNvJzYxb+fCicOO5+4hipGOG/Zsgkk7p8PIVPYjnzKqw8mkdFGc\nZMqbvq7shwR7TuBy3ZkrdnYc05XXpaOvk+F+EbE0PcxIwbNel4bwZ+TAB6zNd5n67Cf2w5sUQ/MZ\nyibjy9WuwDEiFU86b4QjKZD2mvGz/A1X3kgtPkiBl8jHl7axhxcRcQh7EwJS6S1U3EheHxGkY8Ni\ncHbtCeRl/SZMHl95hJmH5yH/tzZpURUyew1IS8eQQmj42zDUalsH5etUVKqS1FTpnNzYb8seRJzE\n48uPUKZGOfgyojcRZU3KFuPjXN11QgWl10ciUl/deRG3Dt/g45smPkpamGl1rVD5kcERnEx98yC7\nVhmpmsqia5pIxDXa1IJuPl1yk5tpxRKwZ+q/Y/6coPSQQ+6QTqBQMSM0Y8TZ+l0bsvHtzf43JKFy\n46rp5Eo/2eX6Yz6GAzz9EREUjnwF88GwWCF5+4kwTv+fiABLJP9nt13RZmh7EHlc0Z7bu8Fm8zlO\n9K5Q15ypT+dDnY712TV3BEHeASBy7LvXwRi8dLi8bMX8msI32P/RKhWqoE2bNprcRJpAQCAgEMgQ\nAs+fP8eFC+cxdlbzDOUTzv8fCIQGx2Hn6ju4fYVUyP/CY6bwan/tJS6ffo5T+x+DiJaHtz1gS6Dl\nxMyVHfDCJRg7LO8g0C8akWGJKMaUQctULIwmbcrDwc6Hk16JtOr3KhwzV3RA8VKpJ8hLljVCsH80\n+gxXJrEUZQTaR3dew/qIK968isDEea3wxNEfd21fonjJgrCoVhQ6enlw6QT7Lj32lBNuc+XOiX1/\n3meE2whERbD2lCwAs3JGKMhIl/WalsZ+lhbMiL1EcN335z10ZoTUvsPqcNLslhV2eMGIvZHhCShT\nwZgrpW5ZZsfazsoKT0SV2sVRuKh2hJAixfKjU59qaNnZAs+dAxmx9gNIHfbv2Lxx53D7sjeI1Opw\n00e+Od9/g3Y9KnPM6b6Mzsn2Vbd5X9Yd7IeqtdX/drh/4xX2b3KAr1c4QoJiYVBAl/dPL18epWbG\nMBLxqllXMG9dF67Sq5TIDh7f84MVGxPm1YuCFHaJ6Ern4DQbL6FBbMWGu37oNbgWGrUqp5pVHP9g\nCAT5x+Aiux5nz54NPT1BdP7BTq/oThYjIN1P/T6sexaX/GMXFx4dhyELt3FFUyKdVi5TAkUKpbxc\n+pHN6+w6dxPXHrgxdbMvuMeWry9V1BhlTYvgjwPnQeqh7KsS1x8+Q1Wm1Dq6V2sOmK3jU7i98ufh\ne65esLp8D5Ex8TiyYhLy5s7N42tblGYEGkZOYYqmR67cx6HL9kxhtRymD+rC559y5PgJnZhKLKmG\nEqnyOFOE/cDm47bPHQVDRlol02Mv5h675oCjLL8pI+g2rqE8n8Od0vgoXbwwBnRoDFJ+tTxkA1Kt\ndfHyww6m1Lrp+FVUKmOKuSN6MnWtnLyEdg2qIYzhtdbKBvrsxWJXbz9cdXgKq2UTWHvycR9Hpgy7\n5YQtD/sxMu5z3wDMG9mTq8ym0YxU0cWMDfFz24bo2qwWHru/Rjybw81Iv1IVyCJa16uCwLAo3rco\nJp5gy84nkVTbfSMRS3nsXTyxmbW/RgUzrgSsbT7KT4q8M/48jD9nDJOfH6lcIgDXtiiDNQxnwpgU\ndVfut+ak3MGdm0luYp9BBDYeu4o2bduhbl0ZWSeD2YW7QEAgkEEEThw/jtw5/kKvTqnJXqpFkarq\npj2HcZAtnU6qor5vAphy5wc0qS+bxyZFyKXrtsHz1WtOcvT28WPqicVQgi3T3qFFE74E+uqte9j3\njS5cGHn18o07OLZjPQoVLABSjSQl0PjERLwJDOIESNOiJlwZlOJpyXYikVK9pIL5y/gZnMhXhRHw\n7jg84mRNIhzuOHQC86aOhT77nZFenUT6W7RmMx4/fc7aFomK5cowtXg9LLTcxFQz/Xhc3RpVUb5M\nKTg4uXDiKRF4+3XvxJYwL40AplB66eZdnLl4jfmYMcJcO5xkZFgis5Eq65qte5nyaQBCWdklTYty\nH1VMVY8J45Ubd+LURVtOyCVyph0jFJIi6tlL1xlhNIzXs2f9MhA+kjm5PsOabfs49uER0Rw/Ij+S\nOu6TZx7scd1fjIB6H9UrVcS4If15Nn320oxqv6qYV8Ct+w9x4OQ5vGTnb8msyZy0fJn104yRUomA\nvG7Hfk6EJUIf4XPF7i7HnZN82TPOJvVqgc6LadEiuGxnj1OMkEz5h/frheH9e/G661Svwvt39vIN\nWJ06z86/NUitd9aEURl+XveJEZd3sTGyee9hTsr0Y8ThL+xlJcLHsKABr49Ivy0a1sPa7fvgz0ir\nRNQlvH7p2VlOvpaw1LQn8jGRKYlwSgTQCEbyLmtWgpOKifDZqG5NHDlrgwu2t0Ak2To1qvBrIL26\nixgbaRxPfbp2QO8u7TlR2JKNK1L0pevMmJFLSfW4oIEBqrFzS6q7ZKTqS9dmj45t2LguzeMIAzdG\n+lzIiMm5v/lRQnp107gmwjIZka7fMtJ7uxaN+bnnkd8+HJyeYOPuQ/RomI97IolTXUWLFFZ00xgu\nzsZM/x6duEoqjek4Rrxt+u3/i8aMaSQSIZxUapet346rbCwGBofAl12TlgtmchKvajYixk5d8Ac2\nr1jA/y8ppmtT1ls2tmYuW4ttTPH23kNneLFrKPBdCLb8sQB5GalX0e46PsafDC9S4TVh51/bfIpl\nqAu/YeP/CBsfYr5FHToiTiAgEMhCBHx+Yg8Y6H++MIGAQEAg8J9EwMfHBxUqVMA6xy2MwJX9DzXp\nXy4tW04kNrMqZUBEwOy2rKqTyol6F8kJuVKb6U3Ej8kfOWGR4siHiJi0vP2/xUgtltoZExqtVlE1\nu/tFKrhEwMhfSD3h4ANTywj1C4FJaRPk1ZP9YMtO7BKZ6imRHjm5WoE8nJ11qpa9tPt8zLSaC31G\nulRndM7ofBG5mcYTnaOMjKnM9pHq0XR9Egk7kl0DlRpV4e2jc/eBkcZJDbZUldLoPaOfUnfcbrni\nBSOmD2Lkz6wwIlBnBIe/UycRpeOj4lCEEcjzsIdcaVlEYDiMS6SeLCA1aL0C+sjFFEcyY7ObTEG/\nzj/jjz/+yEx2kUcgIBAQCKhF4OjRoxgxYhgehy5Qmy4iBQKKCBDRlCbniRSryUglVFcv9b0v3f99\nSGZp+rLvUdl98lf2XS4jdlCZ8Ux5NgerQz+/8oSzuvoov78PW2GBqbxWqFyEq7Oq88vqOCLbKrY5\nq8uXyiOVXVLJrVyzuFo8Jb+M7ElNNzggFuUsUt+rSOWEBMaiqMI5JrIwkWZNzQxB6r7C/hsIODGy\n/NieVoiIiICRkdF/o9OilwKBTCIgu58ajqhbezNZgsiWUQQ+07wWuw8IjYrlSq2K+UmplQiv0Xf2\nIZAppRrk04OBvvKLuZJ/MiPnvAkOh1kxY66WKsUr7mPiE3ldhb6RTBXTYtlcJhFlSc01s/aFvajt\nGxSKIEYQLVHEiJN7ScVenVF9pMZK6rSmRQopuRAm9EITkYpJ6bQA6/ffNSIIU1lZYdTPyNh4JYKz\narl0vkqwvimaNvlIdfdtSAQnDSvmVQzz+S2m9vuB9cmMkajTwlgxjwinjYBZ18lYuXoNxo8fn7aT\nSBEICASyDIEe3btDL+cXHNq8OsvK1FQQqXJ6vPTlZNASxYpqcs2ytKyok/+vZ+qnioRTaiCpYubP\nJ3t5hY6JnJs3b9rz++TzPY3UQGm+JCQ8Qq2iqrp+0fM9UrkkAjEZ+Xyi7+1MPIeksogoSoqokmCL\nYv+pHj//QJQuZQoij2a3UV9InTOB3fcQifp7nitt6tZmPIVHRjGsdPj5IcJxPn3192XvGfmV+kdz\nbZIR3hKRVYqT9trUTb6dB43Fse3rULCAjFgs5aexRuRbIpITSZZIuX/XSF04M+NOtV5SLaZy0htj\nRIj3D3yHyhXT5i+kVxb9D3gb/I6fI9X/F6rtCmBk3JLs5QCyjORTLUfx+DZ7GaDjgDFivkURFBEW\nCAgEsgMB26z5NZ8dTRNlCgQEAgKBHxABuqknNcjvaVlVJ5VDSrSKRj8OSalTMvLRRM7b/dtWyTXN\nfbuRnbKUiJyTTYbTH5E91VlW9EtduVJcWgRPKT0vU7EgVVNFe2LrBNo0WSF2LvrO/kWTi9o0fUZI\ntGhYOVVadtapWNmb568Z0bdomsRX8qVzJqn6ZoY8mVYfFduhLqzpWiGl3C3jNmCX9yG+5AypL0tW\ntXl1ODCFX1W7ceAqhq8aoxqd6WNN11amC00jY4Ei7A1dtqVn6oivlMfAWDNRKL1yRbpAQCAgEBAI\nCAT+aQRIoVUbU0d8pXxEDpGIr3Qsu09OIb5SXH6DlPtoOtZklL80U3X93vY9iK/Up7LmaRNUM9tn\nwl8T8ZXKVSS+0nGhwvp8o7AwgYBAQCAgEBAI/BsQIJInGZFANZkqkVLVV5eRDUhlVZMVzJ9CllH1\nUySYkuIsqdRqMlJWna2iEEwkzIqlivFNU15Ko/oaVqug1k3CpLChMslBrbOWkVlFfKXqqJ+Kyr7q\nmqDufGmTj9Rw0zuPfH5LhTCsrg0iTiAgEBAICATY9w0jxJE65ve0rKiT/69XUFqV2q9IfKU4TWTK\nyfNXSNnS3I8e2JcrQabpkMEESQm0FFNtVWfq+kXPISXiK+Uhn8wSEKmstOqmsomIWZmpxKpadmFF\nfTFnCr+qdoWpgl69Za8arXRc3KRIqqXrlRzSOUirbsVs2oynwkYpLymlRXylMnV0Ur/4nRbxlfy1\nqfuZhzdIUViV+Er5pbFGSrJZZZkdd6r1q2uvqg8d07jXRHwln/TKov8BFZgStDYmEV/JNyP5tClb\n+AgEBAICgexGQJBfsxthUb5AQCAgEBAIyBGo2ryGPJxWQJDm2LIeZkWRHlakqpmVlp11EnHUasF+\npnZcGi/sn2HOyUVZ2fTvUpb/Cz9Eh0TB7uA1VG9VC4VLFUGYfyheOXuD0nrPlC3Ns2/WTkQEhCO/\nkQEM2JYWOfS7NFpUIhAQCAgEBAICAYGAQEAgIBAQCAgEBAICAYHAD4tAElNy+syW0U1Ies+WRdb+\npZq/C4hZscJoXruSxmIM0lD90phJJAoEBAICAYGAQEAg8N0QaNmofrp1GRsZpuvzX3D43liVLmmK\n9Oo0yK9+ZcUf/Xy4PPPA3D82cKXcuw8e48zeTT96l0X/BAICAYGAQEALBAT5VQuQhItAQCAgEBAI\nZA0CjXs3y5qCfvBSSlYqBdq+p2VnnX+xZWx8nrzEa0aC/XXrVEbuNfmeXcuSuloNboeE6ATcP3MX\nRHAlddpSjMzbekh7/LJwiFzxODYsBk6XHqBmm9qYcXheltQtChEICAQEAgIBgYBAQCAgEBAICAQE\nAgIBgYBAQCCgiMDJ646wc3rBoxbtPIXh3VqgegXtVJ0Uy8lMmJRH01MfzUy5Io9AQCAgEBAICAQE\nAt8PgT5d23+/yv7Pa/reWJHaZ3qKn//nkGa6+V//+gpntxdwee6BnZZLQERhYQIBgYBAQCAgEBDk\nVzEGBAICAYGAQEAgIBDIVgTK16kIq8BT+Ikt/0vLyvw/Gi0B031Kb759/vQZuXKrv4WafmgOJu+e\ngdx5c/8/dlO0WSAgEBAICAQEAgIBgYBAQCAgEBAICAQEAgKB/wMEOjauiQ6NUlZYyptHzEP8H5w2\n0USBgEBAICAQEAgIBAQCAoG/gUDdGlUR+vw+f9b4//q88W90X2QVCAgEBAICgTQQUM/cSMNZRAsE\nBAICAYGAQEAgIBDIDAKklPqjWFrEV6l/gvgqISH2AgGBgEBAICAQEAgIBAQCAgGBgEBAICAQEAhk\nBwIF8ullR7GiTIGAQEAgIBAQCAgEBAICAYHAvxqBXLkExelffYJE4wQCAgGBwD+AgPhm+AdAF1UK\nBAQCAgGBwI+NQIjfO5yxPIEBC4fAyNQ4Q5212XwOeXTyoOPYrhnKl1Hn0DchcL3xBHl086BO+3oo\nUKSgVkWEB4TB64GH3PfLly/QzaeLBt0ay+OSE5LheO4ewvxDUbG+BWq0rpWmUqo8kwgIBAQCAgGB\ngEBAICAQ+A8gEPgmGnvW2WPC3FYwMTXIUI8Pb3vA7hNzof+oehnKl1HnIP9oONj5IK9ObjRrVwGF\nCutntAhcs3ZH8VIFUa1O2svPxUQl4eyhJxg1rVma5Xu/CIGLoz+7l8yJ5u0rZhizNAsWCQIBgYBA\nQCAgEBAIaIXAlhO2IFXZsb3baOUvOfkFh2HNIRssGNUbpkUKSdFZvv/w8RPuP/XGs1f+aFS9IupX\nKZfhVYciYxNwwOYOZg5JmYtL/vARl+xd1LZXTzcvujStxdOeeL7G68AwtX71WFtKFy+sNk1ECgQE\nAgIBgYBAQCDwzyCwcbcVdNgzuPFDf8lQA177B2LV5l1YPHMiShQrmqG8GXH+wO5B7B86w83DC03q\n1UaD2tW1vre5YmeP+IQEeXUBwSGYMHwA9HR1eVxMbBwOnLRGQNA7dGrdHK2bNkDOnGkL1yS/f4+L\n128jOCQcFcqaoUvbFvKyRUAgIBAQCAgEBAL/JgQE+fXfdDZEWwQCAgGBgEDgh0DA76kvbh+5gca9\nm2aY/Hrr8HXo6OtmK/nVesNpRnx1xvjNkxEbHouFnX7n4cpNqqaL/+GF++Fwxl7Jb9OTXfLjoJeB\n+KPvYoxcO571vxmcrzzCxGqjMGXvTFRpWk3uJwICAYGAQEAgIBAQCAgE/osIeLq9w4VjT9GuR+UM\nEznPH3WFrn6ebCW/7t94H46M+Lrwz26IikjEqG4HWbgrajcy0/p0ubsGY/64c/h9dSeN5NelU2zg\n9jhQLfk1OjIJm5beRHhIPBZs6IpiJQpoXb9wFAgIBAQCAgGBgEAg6xCwumyPfHo6GSa/PvX2x5Er\n99GrVf1sI7+GR8eh1bhljLTaDUO6NMfGY1ew7vAlnFo9VWuSCCE1yXI/Hr3wUSK/nr/9GGNX7lEL\nZKfGNTn59a+//sKIJTvgFxyu1u/e3iUsXpBf1YIjIgUCAgGBgEBAIPAPIXDwlDW7t9HLMPnV9YUH\nrE5fQJ+u7bON/BoWEYlmPQbj90mjMbx/L6zfcQCWW/fg3P4t6d7bePn4odeISUqo/tyto5z4GhUT\niybdBqBhnZqMzBqG7QePo071KnC4eEwpj3Rw4dotLFu/DVNGD2Hb4HTrl/KJvUBAICAQEAgIBP4J\nBAT59Z9AXdQpEBAICAQEAj80Ao16NcWBN8dhYJzxh/Sr72zETzl+yjZ8iPR6dPFBrLm/GcUrlOBb\n98m9YDlgOTY82KaRrBv2NhRfPn3BTs+D8vblZuofBU0M5ccHft/NSa51OsgUyZr1a4mnN5/g+DIr\nrLi+Vu4nAgIBgYBAQCAgEBAICAT+iwgQ6fX2q1kwNMr4UsVHbozO1vtEh5s+2LLcDsdvj4VZeSO+\nDZnQCNMGn8Qp+/FakXWTEz9ip+UdfP78VePpJcVXXy/1RJGgtzEY1Ho3mrQpj22nBmksRyQKBAQC\nAgGBgEBAIJC9CNzZvZiRHTI+T9WrVT34XdwC44L5s6WBX79+xaD5W1ClbAkM7yZTIVs67mdU6z8L\nS3afwbLx/bSqlxRfPf2CUvleuueCy5t+R22LMsiTO+UxWrff1qBHy7rc/7azOzowIuykfu1RzDhl\nbuz+Uy9MXnMQNc1LpypXRAgEBAICAYGAQEAg8M8i4GBzNFNEzj5d2iPo6V0YF0r5zs/KntC9Tf9x\n01HVogJGDujDi14xZyosmnbGQsvNWDn3N43Vbdpjhesn96FMqRLc76effkJhhbaeuXiNEV2Po1BB\n2XPLlZt2cXKr42NXNK4nU7SXKpizYj12HDrB/I+y9lSUosVeICAQEAgIBAQC/1oEcvxrWyYaJhAQ\nCAgEBAICgf9jBDJDfKXu6ujrIC9bPi277Nz6UyhToxzKsk2y5r+0xvuEZNw8dE2KUru/tPU8arWr\ngwKFC6JwySJ8UyS+UqbokCi89fRXyp87b258+vBJKU4cCAQEAgIBgYBAQCAgEPivIpAZ4ithRaqv\nOrq5sw02Un21qF6Mb1IlXfpVRxIjtFofUb/sr+Qn7Tcts8Po6c2kQ7V7f59IeD0PQfMOqR+gfPr4\nBbNHnIaBoS5XfFVbgIgUCAgEBAICAYGAQOC7IaDP5qh08+bJVH3ZRXylxji4eePB81eM+NpS3rac\nOXNgUKem2HX2JhKTP8jj0wq8ehuCZ6/80bFxDSWXj58+Y/rgLmheuxJXvSXyK20x8Ylw9nyNzk1l\nBBF9XR1YTh4As2KFebrkd+meK3q0kBFklQoWBwIBgYBAQCAgEBAI/OMI6DPVV10dnUy1I7uIr9SY\ne4+egIioEvGV4nLmzIkhfbtzldbEpCSKUmshYRF47vkS5UqXRCnTYnwrWbwodHRkzxo/fvyEdi0a\ny4mvVMjgPt14WQb59ZXKJMXXP3cfwoalvwviqxIy4kAgIBAQCAgE/s0ICPLrv/nsiLYJBAQCAgGB\nwL8SAa+HHjhteRynVx+DK1M1jY+MU2onvaH5/K4bfJ68lMdHBIbj0rbzoLS37m9wZs0J3Dlux4/l\nTiwQGxYDO6vrilFZFo6LiIWngzvMqpRWKjOPTh4ULVsMjufsleIVDxKi42HHyLE7Jm3GkOJ9sWHY\naoQHhCm68HDDHo3x6rE37h6/xY+TGan20UVHdJ3YM5WviBAICAQEAgIBgYBAQCDwoyHw9FEAdq+z\nx+61d+Fo54OYKOWHE1+//gWne3544ZKiMBYSGIujOx+y+8K/4OMRhj3r7XHppBs/VsQnKjwR54+4\nKkZlWTg6MgkuD/xRoXIRpTLz6uRCyTKGuH7eXSle3YHdJU+uFlvOQrkMRd9PbBWBrStv4bfFbRWj\n5eEtK+zg7hqMEVOacLKvPEEEBAICAYGAQEAgIBDIFgQeMgKp5SEbrD54ATcfPUdkbIJSPeHRcbC6\nrDxfFBgaie2nr/M5LY/XgVjD8h+3dVCa46L5L3sXTzxhZNHsMBt72Ys5VcrJ1M2kOiqXNUXS+4+4\n/tBNilK7//T5M5bvPYtlv/ZLlU4k1jqVyqaKv3D3CZrUMIfhN5JIg6rlUynHUb9t7J3RvUWdVPlF\nhEBAICAQEAgIBAQC2YtAQmISdlmdxILVG3HwpDXcvV/hy5cvSpWGRUTyNCnyM7snuGn/ALfuP0RS\ncjJO29hixcadePn6jeTC9/Qdf8fRCc5uL5Tis+rggq0dL4qUXxWtinkF3i7bW/cVo5XC2w8ew+On\nz1GuQXuYN+kEq1Pn8ddff8l98rAVHCVFWCmSyLKd2zRXIrgGhYRizIyFnDw74pfekqvYCwQEAgIB\ngYBA4F+PQMp6Lf/6pooGCgQEAgIBgYBA4J9H4MoOGzy1c8Gso/Px0skLy7rPR16m1lqhTkUMWjoc\nuZkaxomVh/HwvAPGbpyI8iz+8ZVH2D7hT8RFMJIs+7355oUfC8fi+DIrRAZFoM/M/vwHOBFG983c\nibx6edFmaPs0O+v9yBNfv2heSrZwqSIwLlFYqYzQNyH8B69h0UJK8XRAaq7eDz15Oi2HompfGFFh\n4OJh8HbyhNcDDzictef9mnV0Hmq3ryd3bzeiE+xP3sbmMevw2s0HAR7+GL95Chp0byz3EQGBgEBA\nICAQEAgIBAQCPyICx3c/guMtX6w/1A/PnAMxvvdh6OrlQdU6ppiysA3y5M2FHatv46aNJ+av64Kq\ntU1x19YbSybbgMin9FzilXsooiISsW3lbYQGx2HUtGbsPvErI8M+g+Wcq1z5tedg5eXoFLF0cwpI\nRZpVTKdwsRIFUJRtihb4JprXb2ySemniQsb6eMrKpQcn6u4TqZywd/G4ddETK3f1RkJc2kpru9fc\nxaDxDaGfX/1KB7ZnXzBlk5/wipGAx3Q/hOeMJFyJqdHOXtURlWoUU2yyCAsEBAICAYGAQEAg8DcR\n2HnmBuwev8CR5ZPg5O6LHtPXgpReifi5cHRvePoFYdbGo0wdLQ+GdmnOa7vi4IqJq/cjIiae3xu8\n8A3g4eV7zyEoPBozh3Tl+f7Yb43zd5zx54yhaomkVNi7iGj4BYdr7AXNUDWqnlot3jcghOcralRQ\nKb9xQQN+TKqummz1gQuY8HN75NfT1eSmlHb+zmP0bl1fKU71gNRof2J/RIwVJhAQCAgEBAICAYHA\n90MgOiYOzXoMws41SzC4bzeMmDoP42YtRp3qVdC4Xi1YLpiBo2cvYtri1dBj6u3D+/cC5Zk8fwVO\nX7TFLz07M1LseRQ2MsQpm6vYc+QUE7+x5mqpHi99sXzDdpy7cgNbVi5A3RpV1Xbs4RO3VGRbVcdS\nJYqDVFlVzcfvLY8qVkT5uV5hY9nzPFUyrmL+pg3q4BNTrn/o4gYn1+cYM3MRjp+/gkuHd3D1WEVf\nmts5e+k6I/juwKUjOxWTcO32fcTGxXPMhkz+HQ5OLsjF1GcH9+mO+b+NQ+7c2bcakVJDxIFAQCAg\nEBAICAQyiIAgv2YQMOEuEBAICAQEAv9dBJLikmC1YB/GbprESK65UaVZNdRsWxseju5YcH65nAzQ\nb85ATn6VkKrXuQEjs3aA9YbTKMVUV7tOkqmgzmwymfsR+ZWWL2k9uB2cGVGWlGU12fKeC5Acn6zJ\nhRFVh6LPrF+UfGKYqixZHt3Uy9XlZQ83PrMfx6Ria2CsTIagPAWKFESXCT349uXzF5xYcQTW609h\n2/iN2OyyC/oF85EbCpoYYsX1dZjbehoubT2PivUtYN6gEk8THwIBgYBAQCAgEBAICAR+VASI8Pnn\n4huYv74rJ7nWbVIajVuXhytTU91+epD8PnHc7Bac/Crh0KKjOXoOqYUDGx246urgXxvypF9a7uJ+\nRH6lJXx7DKzJibKkLKvJJvx8BInxHzW5YNKC1hg9vZmST1S4TOVNRzf1NJGOXm52n/iVqdgmw9BI\nTykfHdCDkw2LrmPWyg6p0hQjnB3eIGeuHKjZoKRitDxMZF8i0ZpXNQHhVMBQF/4+kRjV7SBGdj2A\n848mwaS4jNAizyQCAgGBgEBAICAQEAhkCoG4xGQs2HEKGxk5NS9TA2tWywJt61eD47OXsF43g9+7\nEAn2qsNTEKFTss5NanEi7Iajl1GlbAlM7Cf7/m86ajEu3HXm5NdKZUwxZ3gPTn6V8qnbn7Vzwtyt\nx9UlyeOIcBF9Z5/8WAqEMUXaHDl+Aqm0KpoeI+qShUTGKkYrhe+5evF5uIbVlJXVlJxUDkgB9wHD\n5sDi8SopyofWtx+jW/M68ns/5VRxJBAQCAgEBAICAYFAdiGwYdcBfPj4EUQEJZs7ZSwuXLvFSa1T\nRg/hcUP79cTlm2ylHmfZqjqG7KWZveuXc/Lru9BwXDm6C7ly5UKrJg3QZ9QUPHB+ii5tW6ByxXKc\n/EnkV03Wdch4xCckanLB0lmTMWfymFQ+oUyRNkeOHCCVVkUjoi5ZSFiEYrRSuH2LJqCN7JmHNwZP\nnMWVbNfvPIjZE0fJfROTkjBz6Voct76M5PfvUbtdH95nicxLxFmy/j06cXLwhw8fsXLTTqzashuJ\nTBV37aJZ8rJEQCAgEBAICAQEAv8mBJRnBv5NLRNtEQgIBAQCAgGBwL8MgajgCHz68ImrtUpNM29Y\nGc5XnfA+IRm6+WVkACLGqloeRi4lMzVPWY6tZKVSeHrziZKrurxKDuxg3+tjqlGpjnOpTP6Tgw5T\nqOWmRtmVlGRz5ckFfUMZiVXmqP4zZ66cGLRkGAwZ0XXfrJ14Yf9MSdnVzuoaqjStxrdbh29gTstp\nWH59DQqXTHsJXPU1iViBgEBAICAQEAgIBAQC/x8IhL2Lw8cPX7haq9RiInnaX3uJpISPcqXTPOx+\nS9V0dGT3jqUrGMuTypkX5iqy8ggWIOXY9MzOa2Z6LsiVO2cqH139by9HqblP/PLlL+TOkxMGBb/d\nS6rkPrL9ITr1qQqjImnfR8bFvseJPU5YvbevSu6UQ69n7/hBqy4WnPhKB2bljTBzRQfMGXMWp/c7\nc+JuSg4REggIBAQCAgGBgEAgswgEM5XWDx8/cbVWqYwG1crjquNTJCS/lyuiqr13+TbvVdGsuJQV\nFqWLw87phfw4jxbKYOP7tMWonq3keTISyPdtnk01zxe2JDGZiVHqF7spPiY+EbvP3WQk1l/pUGuz\nsX+C+lXKoUgh9eVSQfRC0AWmdrtv0TityxWOAgGBgEBAICAQEAhkDQK+/gEIj4zGR3Z/QwTS6pXN\nmcKrLgLfhShVkCePsjiMjo7s2V1Zs5Kc+ErOlSqU43kCgmXzFHSgmo87qHwEuNxWiUl9mJuRa9VZ\nPr3ULxuT35cvX7i7SREjddlSxVG/H145iaotuuPkhStK5Fd9VscOy8XYtmohtu4/it9XrOfKtw8u\nneDluL7w5BgM7tONH+dlK10umTkJF2ztsO3AMSybPZmtCKB+bihVQ0SEQEAgIBAQCAgEviMCOb5j\nXaIqgYBAQCAgEBAI/F8jYGpekiubutm5yPsRExaNCvXM5cRXeYIWAXqLk82LZ9hIpTW9jQiqqmZc\nQrZcyofE96pJSGbk3eIVSqRaAiWVo0JEkz7NuZJFsG+QPPbW4etwOGOP8VumYOKOaZiw/TdEMtLw\nnmnb5T4iIBAQCAgEBAICAYGAQOBHQ6BMRWMYm+TDg1u+8q5FhiWgWl1TOfFVnqBFIAdTeyUCRUZN\nRzc30ttyMfVVVStqKiNyJCelVo1NSvjASaikQKtqpMx6w8aDK8PaXfQEbXdsvbmb1/N3/Dg8JB7r\n5tmiSi1T3L3qzePIz983khGGP/NjJ3s/5DOQPUApqKIuW72+7OUxv1dpq5yotkscCwQEAgIBgYBA\nQCCgGQFzs2KcIHpLgbAaFhWHepXLyYmvmktQTs3J57gydu+Si81d6TJSRXqbck2yI1NGAPn69S9O\n4FVMT0iSzXkRGVedzdlyHLUtyuCygytXqiW1Wt/AUF4Ohe8+Ub8aEym69mhRV12R8jhSyP30+TOa\n1DCXx4mAQEAgIBAQCAgEBALfB4GWjepzNVOHx7Lnd9Gx7CXlT5/QplmjDDdAmv/I6LwMEUPT20hZ\nVp2VKG7C7m2+gtRWFS0+IYkfSoRcxbS0wkT67da+FXz83qp1oWeTpIbbq1NbPH3hJa+zQP58oE2x\njeRbr1Z1TsJ9zQjGwgQCAgGBgEBAIPBvRED9t+u/saWiTQIBgYBAQCAgEPiHEfiJKWHNP7sUawet\nxKF5e1GuVgWE+L7Db/tnf9eW2Ww+h0/s7VVNRsqrFkyVVtGI/JpXLy8igsIVo3k4LjIOZWvI3mZN\nlZhGhEHhAshXKD+KlzeVe9w+ehO129dlS9rKyLdthraHr8tL2B26jsSYBOgXTFsRTF6ICAgEBAIC\nAYGAQEAgIBD4P0OA7hO3nBiIGcNOYcOi66hcozjevo7Cqt19vmtPDm97wFROPmuss07j0iBVWkUr\namoAHb3cCA2MU4zm4ejIJFhUL5YqniJCg+MQEhgLyzlX5enSw6Hr1u64d/0Vlmzujv+xdxZwVSVt\nGH/WIsWiRQEF7G6xdQ2stXXX7q61u2Pt7u7ubhRFCRNFxQBB6W7Q75uZu+dyL1wuV2UV9Z39Xc6c\nOTPvzPzPdRnOec5zeAznjXfldXgmKjIe8bFJom3R4kaYvbaNOP7sQYqzCi8ws8jDbrxkg56+sjuL\nqEw/iAARIAJEgAgQgS8iwNcuhxeOQtepqzF5zX5UKGaF134B2DJ9wBfF+5JGbs9e45qrarGpFC97\ntt8w6q/m0q58y8W7PPkGhqKohYm8PIRde+IpPfFrcHgUrrqkONTyupExcYiNT8TY5btRwrog6lZS\nvp7G29x64In1E/vw6umm40wg27xWRfZgedoHhtJtRAeIABEgAkSACBCBTCHQu0tbvHrrg2GT5mDm\n2GG4fuce5kwYgSb1amVKfE2CLN+4kz1QoyxeTd2uTvXKqFG5fOpiFLcpIsreMadaG6vC8uMhYWEi\n/zniV96gmI01bItYyuOoyjSoVR3Xb98Dd3jlide/cccFPn4fULhgynWgIpayh5L19fRUhaEyIkAE\niAARIALfnQCJX7/7KaABEAEiQASIwI9EIBdzXW3c1wFVm9eAbh5d1OpQ95sP/97pO4hX4d6qOJC8\nxvnSiF9zstfSNezRBG7n74knSPkTmzzFRsbig5cfus7sKfY1/fHstgf+x55ELVGjlLyJ95O3KFQ8\n5Q9zfqBKixq4sPkswgPDSfwqJ0UZIkAEiAARIAJE4GcjwB1XO/SqjPoOxYSLadN2pb/5FK+e8YQq\n91bFgRQw0k8jfs2llQNtulYQYlXuopaNCU14io5MgM+rUIyY1kgxhDxftY41LnqMlu/zTBwTtNaw\nmIfhrE2H3jKHtJoNbZTq8J1l0y/h1P6HSu1rNCiKR66+SnV5/8nJn9iYldeYSpVohwgQASJABIgA\nEfhsAjraudCndX00r10RefR00L5R9c+O8TUNvN754/h1F7UhcjAhqSrxa48WdbBwx0k4M7dVRfHr\n/edvUcamMGwLmaqMe/ifUWnKp6w9gL3nnfDi2PI0x3jBKUc3lLezhIVJ+q8b5g//8LmsHt9bZQwq\nJAJEgAgQASJABP5bAtyt1NTECBsXz0KB/HnR4vd6clHnf9tzSvSTF68iJjYupUBFzsSogErxa6/O\nbTFv5QbccbmvJH51f/QUZUsWg10GQtbUXZ04fwWtmPuruvT0hReaN0q5x9mtfWts3nMY99wfKYlf\nn714jYKmJkpl6uLSMSJABIgAESAC35oAiV+/NXHqjwgQASJABH5YAtxtdVaryWgzugPi2KtGuPDz\nE7sRn9+8ALhjhpSSEmSurNxNVUpxUbJXkyQrOHHx47wuv0Autef7sREx+Jj8Ue6eKsWQtnMuLpKy\nn71tOawNHPdfhfNxJ9RsW1u0dzpyA1Vb1kD11vZK8XZO3oLosCgMXjsSJ1YcgbaeNur92ZC5x2qL\nMV/cchYDVw2HgaHsNbm8cVUmdL178g76Lh3MRBMyce2Le56wLG0FMxvVr5xT6pR2iAARIAJEgAgQ\nASLwAxJISvyIge12odcIe8REJ4rX8H5k60Rj89zydR6fluTKGhYqWxvysuioBL5BctJHseU/wtnx\nRBZTcZ2YmJDMxKjxQgjKnVBVpW1ne6kq1qis25AaOHPwES6ffIrGf8gebrpw7AkaNC+Ohi1LKMXg\nwtWIsDjh6qp04Ct3/p7dGN0ab8aDu+/kAt17N9/A2s4Qrf5M64zyld1RcyJABIgAESACvyyBxKRk\ntB69GKP/ckB0bLxYuyR//ARzo3xp1i7cGTWZXafK8e9bfqL+fSCbx5BSSEQ0czpLlq9d+GuGeeLl\n6aVOjWuCf74kmRTIiwFtG2H53rP4s6m9GHM8e03wudv3sW36IPk1KR776Wtf/L1sN6b3b4fqZWw/\nu7tjzNG1dV3ZAz3pNb77xAsxcQmol8o1Nr36VE4EiAARIAJEgAhkLoENOw/g6JlLqFimJJLYOsTn\n/QeYGhkit76eUkeJzJk1IiqarW2S2domB6JjZNdnpLULrxwSGi7axMXLrtfwHd6Op+B/nVjFTqof\nVw9vT1Wi+a6psSEG9+iCJRu2o2v7VrK1Dev/zOUb2LV6odLaZuLcpQgNj8CGRTPx4vVb8Ll3Y23K\nl5Zdu3n6nK1LmAh34vD+YgBx8fHgrrStmtRHqWKytVBIWDgeeHji2NZV8kFWr1RO9L3z0Am0a9FY\njIFzcrrnjrkTRyqtEeWNKEMEiAARIAJEIAsQIPFrFjgJNAQiQASIABH4MQhwMaeJlSk2/71OacC6\nBrrouaA/GnZvjBcunkIoyis4HXFEkXJFmWhUhwlCb4s2RxYdQJdp3eDh+BjPbj9BfHQcDs7fi1ZM\nlHpt92V43HosBLF7Z+xAq+Ftkcc4r1JfX7tjXNgEsy8swqbRa/Dq/ktwh9gg30D0Xz4kTWjXc3cR\nxcSvHz9+hPfjN7jBRLN7Z+5A7Y71kT1ndjgMagW7KsWV2vVbOghbxqzH6OpD0Ii5zPo89UZEUDjG\n75+m9Me5UiPaIQJEgAgQASJABIjAD07gN+aUWrBwXiwYd05pJvoGWhgzpwn+YK6qj5mj6Y7VsjUh\nF5UWL2sKXb1cuHr6mWizeelNDJnUAK633sL9jjdimYh2wz830HVwDZza9wBuTt5ITPiI1XOuoPuQ\nmshvpHwDR6njL9gxL5QXW8/0wvyxZ/Hs4QcR3983ApMWN08T7cb550L8+pGJZDLz1b42JYyx43wf\nLJ58QYhfuSPtw3vvsPF4d3ZTSrXgN83gqIAIEAEiQASIABHIkEA29hC3lZmhEIUqVjZg17DmD+uC\nDswFdsepG7j1wJOJWpMwc9MRDO/cFJ5v3+PUTTfRZPGuU5jaty1u3vfE7YfPER0Xj/nbTqBB5VJY\nc+iCqHPkyl2Usy2MpjUz/yGWuUM6ifVBxwnL0bBKafiHhGNcdyb8KGalOCU8e+Mn5vHwhfdni1+5\neNfx/jMsH9NDKWbqHS6QbWZfHrly0i231GxonwgQASJABIjAtyDAXV+feL5E4059lLprUKs6ti2f\nhzwG+ti67ygcnV2RwB6YmfbPKvTv1gErN+8W9S873hZC0wpMQLpw9SZRtvfoadSrURXJH5OxbMMO\nUXboJLteUaoEHBrWUeonM3YWTPlbCHLb9h6GRnVqwj8wSAhYKzBBr2I6c/k6E79Gint3MUy8y8Wq\nq7fuQd0aVVClfBnky2uASwe3IGfOnKLZJ2bkc+zcZcxYvBqVypZCk3r2zB03H07uWAt9PV3F0NjI\nBLVTF65E16HjYF+lIm7ddcOkEQPQpU3aa0NKDWmHCBABIkAEiMB3JPAbcxH533fsn7omAkSACHxX\nAl5eXrC1tcXi26tgXbbodx0LdZ71CXBXVi7+bDagJaJCIxEbyRy54hMRHhCGQ0zAuvrRZuT4gS5y\nRwZHQDePXrpjjmPC3I/MxUM/X25xciICw8W8jZkAOBd7NZ66lMBcQ4J8ApHXJJ+8vbr6dIwIKBIY\nZz8cHR06YN68eYrFlCcCRIAIfBWBPXv2oFevHnAJmPJVcagxEVBFgLuyrp57FZ36VkUEc23lbq4J\ncckICYwWAtaTbsPYTYfsqppmybKwkFhw4W56Y+bCXO4AZ5BX5z8bf+CHKGjr5PhP+/jPBk+BfygC\n9xzfoP8fOxEcHIwCBdJ/pfUPNSkaLBH4jwjI1lM9EXp183/UA4X9VgS4oHUWE7T2b9sQoZExiGLu\nrnFMCBIQGoEFTMD6cP9C5GRuaD9C4g/jhEREwTh/ypuJUo/bNyAEFiaf//947ubq4x+MEtYFU4dU\n2n/7Pgi5mXC4QB59pXLa+fEJWLYYhrkL/sHAgQN//MnQDIjAD0CgdatW0M3+ETtWLvgBRktDzEoE\nLjvewXv/ANSsWhEBgcGIZQ/lxMTFCTfY0sVtMW6Isig2K4099Vi4IU0wc581MVK9duFutUns3h0X\nufLExbzc6VZXRxsFTU1Sh5Pvh0dEIleunKxextdyEtlakccsUtiCjG3kBCnzuQSuOd1F0y796HrL\n54Kj+kSACHwugfM/xtWLz50W1ScCRIAIEAEi8B8QWNF3EYpVLQFjSxPxUewimjmkZv/39W+K5Vk5\nb2CY/k0BPm4dfeU/gLkLraZOtFq62rAoXjgrT5/GRgSIABEgAkSACBCBTCMweeAxlK1iIdxfuQOs\nYooIi/vhXEvzFVB2/lCcD8/r6qt/ECp1/S/ZNzaTPYD1JW2pDREgAkSACBABIqCeQL85G1G1lA0s\nzYzER7F2GBPD5sj+4zy0w13o1Qlf+dy+RPjK2+npaGUofOX1rMyN+IYSESACRIAIEAEi8B0IuD96\nir5/T8Er54vs7TTZYWOVcm+qHnNDPXz64ncY1Zd3yeeQnvCVR03t1qqllQu21pYZdpg3j0wsm2FF\nVoGLZBU5atKG6hABIkAEiAAR+F4ESPz6vchTv0SACBABIvDDEXjp8hxh/qEoVq0ECtqxpx2Z2PX1\nfS943n2KgrYW+I29Mo4SESACRIAIEAEiQASIwK9H4LGbL4IColCuSiFY2RkywUg2PH34Hg/vvYOV\njSGtE3+9rwTNmAgQASJABIhAlibg4vEK/sHhqFq6KOwKmwmx6/3nb3H3CXtLVmFTWrtk6bNHgyMC\nRIAIEAEiQAQUCTz2fIEPAUHYuu8oGtSqDksLM7z1fQ/XB4/x+NnLH8r1VXFelCcCRIAIEAEiQAQ0\nI0DiV804US0iQASIABEgAph8ZCZOrjqGpT0WIOhdIPKbF0ClJlXgMLAVCpeyIkJEgAgQASJABIgA\nESACvyiB1fv/wq61dzC+z2F88I2AsXlu1P7dFl36VYNNSeNflApNmwgQASJABIgAEciqBI4sGo1V\nB86j5/R1eBcQAnOjfGhSoxwGtmuEkkUssuqwaVxEgAgQASJABIgAEUhDoHuH1ggLj8TBk+cwesYC\n9lBPDpQuboseHf/A9L+HCBfTNI2ogAgQASJABIgAEfhpCJD49ac5lTQRIkAEiAAR+K8JcIHr0PWj\nRDdJiUnIyV77QYkIEAEiQASIABEgAkSACHCB68zVrQWIpMSPbJ3447wqmM4eESACRIAIEAEi8OsR\n4ALXdRP7ioknJiUjV066VfTrfQtoxkSACBABIkAEfg4C/K2MI/t3F5+kJHbvLifdu/s5zizNgggQ\nASJABIiAZgToioZmnKgWESACRIAIEAElAl8qfOWOsW7nXfD6/ksMXjtSKWZW2omLisXNg9cR8NYf\nZkXNUbtjPWjpaqcZosetx40AZTsAAEAASURBVPB0fgotHS2UrlMOVmWs09SJConEvTPOCGZztyxt\njXINK0JHXydNPbfz9xAbGSsvD/YLgsOAlkr9cn6ed57K63z8+FHEqtaypryMMkSACBABIkAEiAAR\n+J4EvlT4yh1jb158gacPPmDGylbfcwpq+46JSsDZw4/x3icchazzo1n7MtDRTXtjyc87DE5XvKCl\nnVO44OY30lMZ15HNOSYyQX7M3y8SnftVlccMD43F9bPPhaOuXSkT1KhfFLr6ueT14+OScO2Mp3xf\nMaOjlwv1mhWTF2UUS16RMkSACBABIkAEfiECXyJ8TUpOhtOD5zh3+yEaVCklXGOzOrKAkHC88PmA\n2hVKqB1qSEQ0tp28jjHdWqRbLy4hEWduuuNDcDhsCpmimX15Udft2Wu89g1U2a5KqaKwMjdKc+yx\nl49gmZMJkJsy992CxvnT1OEFR6/eQ2FTQ1QuWUTlcSokAkSACBABIkAEkKnCVx+/Dzh3xRHuj59i\nw6KZWRZvVHQM9h8/i7fv/FDUqhA6/+EAXZ209+AUJ3D41AVYFjJHlfJlFIuRwNY4js6uePjUE/ZV\nKqJaxbLIli2bUh1pxz8wGM9fvUHdGlWkIqXtGx9fXLzuBB1tLTRtUBvGhgWUjtMOESACRIAIEIHM\nIkDi18wiSXGIABEgAkSACGRAIC46Tgg3Dy/cB/4kalZNfi98Ma3peOjk1kGQTyCSmQPI0SUHMffy\nYuQzSbkAv2n0WiTGJaDvkkEIeheEf/6cg6b9msNhYIpY482jV1jRdzEGrx6BWu3r4OyGUzg4fw+m\nHp+DfKYpsXyfv8O89jOUkNiz+qkFt7umboXTYUeleivcNijt0w4RIAJEgAgQASJABH40ArHRiXjg\n7INNix2z9Drx7ctg9Gm5HXr6Wnj/LpytEz9h6/Jb2H6uNwxN9OXYedltJnyduqwlQoNjRJupy1qg\nYg1LeR2eefMiGMM771Mqa9K2lFz46vnYH5MHHsX05a3QpG1p7N90D+sXXsfaw11hZJpbtLt88imm\nDDquFEPaqdPETi5+1SSW1I62RIAIEAEiQASIgHoCHq98cfSaixCJlrAuqL7ydz4aFBaJZXvOYtOx\nK+jZql6G4tehC7fi7hOvdMWvpxzdMHfrMQzp0ARDOjaWC0L+97//odeMdXjzPkjljG9unsHKU8Sv\nweFRmL7+EBPQhmHF2J4oZJK+IMTd8w36zNqARSP/IvGrSrpUSASIABEgAkQgcwlEx8Tijut9zF+1\nEb+x/7Jq4uLT3zv2Rm49PXj7vUcSu5+3aO0WXDuyE6bGhiqH7fbQAz1GTMSymROUxK+BwSGo3bor\nxg/ti56d2mDJum1YuHoTjm5dJV/v8IBBIaFYvHYr1u88gD5/tlMpfl3Ejl+8fgtrFkxDUHCoGOOa\n+dNQq1ollWOiQiJABIgAESACX0NA9WMaXxOR2hIBIkAEiAARIAIqCXC3U+6galslxX1KZcXvXLht\n/EZMPTEHqx9uxsYXu9CwRxMEvPHH3hk75CNzPuGEy9vPo8e8vkKgalGsEHrO74stY9YLJ1he8dOn\nT1jVfykqNa4Cu6rFRb02ozogp1YurOy/RB6LZ06tOoaZZxdg/bPt4rPBcweGrh+tVCfQJwAfkz7K\n6/C6W17tAe+bEhEgAkSACBABIkAEfmQC3MmUO6iWqWSRpaexaPIFrDvSDSddh+Gix2i06VYBvm/D\nsGrOFfm4nS57YdXsKxgztwksbQqgQvXC6Da4BkZ1PYAA5uqqmHatvYNNJ3vg3KOR4nOebWet/kNU\n+fTpf5g2+LhwjS1bxUIIYnuNsGdOsjkwlZVL6Spzfd14ojtu+0yEi/8U+Yf326iVzNlN01hSTNoS\nASJABIgAESAC6gmUL2aF/m0aqq+URY76+Afjz6b2iE9MynBE3PH12Ru/dOtNXrMfvWeux5apA9Ct\neW0lIcg1Vw80qVkeTw4uQsjVzfLPiaVjhGMrZyYl7w9BqNx1IhLYq5mPLv5brfA1hj14Pm/rcSSz\ntx9RIgJEgAgQASJABL4NAX09XXRq7YCqqZxRv03vmvcyduYinNm9AR6Op/Hm3mX06twWr72Zwc0/\nK1UGiYmNxexla5HMXPwVE7+f12nAaJQuboveXdrBMH8+zJkwAh7P2YPNC5Vjefu+R9f2rRCfkPIW\nH8VYF5joderCFVg0bSzsiljBvmpFjOjXHR36jYTvB3/FqpQnAkSACBABIpApBEj8mikYKQgRIAJE\ngAgQAc0JZM+RHVn1QdFX91+iTqf6sCpjLSaUxygPukztJhzInt99Jp/khS1nYWxpAv18MsctfsC2\nkkzUe3TxQVHvxT1PeD95A+tyReXtRL3KxfDo6n3wvngKCwgV9cyKmsOokLH4GFoYIZd2yutseb3T\nq4+jwu+VkMcor7xeXpN8/BAlIkAEiAARIAJEgAj8FASy52CXabKoocjTB+/h0KEM7EqZCNb5DfUw\neGJ9tk4EHt57J+fPXV+LlzUTH6mweceyiI1JxLHd7lIRggOi8cIjAIWt88PMIo/4mLItF7fy9MjF\nVxwvXsZM3oZnSlcsCOfrr8HHk5T4Eb1H1kLV2tbgAuKcubKLT2R4HJ64+8ldXzWJpdQJ7RABIkAE\niAARIAIZEsieXXZ7KQu/3EjMoVKJIrCzVF5PqJrcSx9/PHrpjaY1y6k6DO74unL/efwz4i+UKpr2\nQWw9HW0sHNYFlmbsmlbOHPLP6Zv30bpuZXnMRObI1n3aWuQz0MOKMT3l5ellpm84hLHdW6Z3mMqJ\nABEgAkSACBCB/5BAjhw5suwbetwfPUWXNs1RpoSdIGBUID+m/z1EjNfZ7YFKKlMWrMCEYf3THLt5\n1w23Xe4L4at0MHv27OjGRK5rt+8DF81KqXK50ihWVHYPUSpT3C5aswXlS5cQH6n8zzYtEM1ibNt/\nTCqiLREgAkSACBCBTCMgu6OQaeEoEBEgAkSACBCB70+Av2bM4+ZjvH30GtnYhfiCdhYo17CifGAJ\nzDHB4+YjvH7wShyv26UBCpinvP7D19OHCTLDUKp2Gdy/6Aq/F76o2bY2uCCTP/3oeecpnt97hlL2\n7OY/czSVUohfMFzOOKNJv+ai/weX3ZDfvIBwTtXS0ZKqpbt9yAShL12fQz+vPuzb1UHuAgbyuhnN\nSV7xKzPGhU1QpLyNUpR8pvlRpIINhGj33yN+z98hV6o58fFyQeyzOx6i1vuXvmLLx66YbCrZit1n\ntz1QtIItzq47Jebdv1h30b7DxD9R/69GShcUosOicGXHBcTHxGPT6LWo1rImus3pLUSwirEpTwSI\nABEgAkSACBABdQT4usTVyRvPH/sje/bfYGVriBr1Ux7UiY9Lguutt3j26AM7ng1ctGlinrIme/08\nCMGB0ahsb4Vbl1/C+2UIfm9dEly0yd1FH9z1YUJQX1SqaQnuViol7nh6/fxzdOxdWfR/+6oXjM0M\n0KZrBWjr5JSqpbvlYs/Hbr4wyKuDJm1KIW9+XXndjOYkr/iVGfPCeVGinLJwxMg0N0qWN2frRJn4\nJSwkFu53vNGys7JohAtaC1nnw8XjHhg4vp4Yyb6Nd/HEzQ9NyiwDjz1gXF206lJOvgZ86xUs6qVe\nS5Zi4lfABfedfUTfXAybOl05/UycA86LJ01jpY5D+0SACBABIkAEvjcB/nvw1gNPJsr0QfZs2YSI\ns0GV0vJhccGmy9NXeOL1DtXL2qJVHeVXyXq+fY/A0AjUKl8MF50fgddvU78KLEwKiGtcdx6/xL0n\nXrBnx6uWSrke5BcYijO37qNfmwai/8t3n8DcKB+6t6gDHfZGn4wSd0F18XiFvLn10K5hNRTIoy9v\nEhQWifO3HyIoPBLW5sYoX8xSbOUVvkMmibmfzd58BGsm9MbcLWlFGe+DwjBo/hbh0NqDMVCVqpVO\n4Scd59cRTzq6YvfsoVIRZm48DHfPN1g9vhf0Ul1bk1f6N3OSCW5tC5mihHXa9U7qurRPBIgAESAC\nROBHJMDXOo7Ornjo4cmuw2QXospGdWrIpxIXH48bd9g1gMfPxPG/2rVAQVPZQ7m80rOXrxEQFIw6\n1Svj/LVbePHqLdq1aIxC5qZircMFnc7uD1G7WiVUq5hyrYI7kJ6+dB0DunUS/V+64QRzFrdX5zbQ\n0daW959e5spNZ9y7/wj58hqgQ8umKJAvr7xqRnOSV/zKjGUhc1QoI3vjjRTKzMQIFcuURA5uwpMq\nHT9/BbZFrFDSLuU6mFTlBDvGE3d+VUylitkiNi4O56/eElwVj6nKB4eG4dY9d+EMq3hcW1sLRS0L\n4cjpC5g6apDiIcoTASJABIgAEfhqAuT8+tUIKQARIAJEgAhkNQJ7Z+6E/+v3aDH0D9hVK469s3bK\nhxgXHYehZfsyV1EttPm7Az4mf8TkhmPABbFxUbHYMWkzRlQeiHMbTmHz3+uE0PXe6TsYVLIX3C64\nYEWfRULgenbdSUz+fQxeuHiK2I77r2FUtcGi/caRa3Bj3xXhZrplzHpMazoeyczVIb2UxF67tnbI\nCkSFRKJys6p47PgQwyr2x7tnPvIm6uYkr/RvJvRDCLiwVO3nX4Fq6rZcwPqbCsuOEN9gVGyc4lKh\npauFD15+iImIUQphWsQMsayMs+SMeZIcXqWKJtYy0USwb5AoKlmrNFqPaIfiNUqCC4jXDFyGWa0m\n46PC69w+Jn3En9N7wL59HeH86nTEEcMrDoD7RRcpLG2JABEgAkSACBABIpAhgdVzruLd61B0HVSd\niVMLYc3cq/I2sdGJaFV5FbSYGJW7iSYnf0LPplvBBbExUQlYMvUi2tZYi/2b7mHBuLN44PwOV894\nwqH8Cty8+BKTBhzFtbPPsW/TXfRy2IrHrrIHgc4ceoT2tdZhKWs/d8wZnD7wEC+Z4+nC8efQt+V2\nJLF1TnqJO5vOHHES4UxUWqeJHVxuvsEfVVfjladsHcXbqZtT6riBH6KEaJQLR9V9Urfj+1xwq2qd\n6O/HBDWNZGIP37dh4M89GZqkvB1AisWdYn0Ye0nMygXCPYbVRPnqhRD4PhLTh57AwLa72Brwk2ii\nrS0TBXswh1fFZGElc/7/4BuhWKyUv3TiKRq2TLkB9DWxlALTDhEgAkSACBCBb0xg1qYjeOUbiCEd\nm6AqE1fyfSmtOXgBwxdtR5cmNTGgXUNMXLUPm47J1jZRsXGYtHo/qnSbhA1HLmPM8t1wZkLX0zfd\nUarjGFy48xB9Zm/AGeZKup4dbzxknhCr8tgHLt5G9Z5TMHnNfoxashP7zt/Gk1fvRIxmw+aDC0XT\nS9zVdOjCrQgJj0Iz+/K4eZ89kPLXBDx74yeahEfFoO3YpUKAO6JzMyEMffDcO71wuMuEubcfvVD7\n8Q0ISbe9pgcWbDuBwR0aI7eu7MGZ1O24cDgiOhZFCzFRzMz1sP1jJEq0G43Z7Hyo48HFxb+x/xSF\nsYcuO4uHrDxe+cJh+AKY/N4fTRj/B8/fKnX7ITgMJ2+4snPbSKmcdogAESACRIAI/EwEpi9ahVdv\nfTC8bzdUZ+JUvi+l6JhYlKzdQohRxw3pg+SPyajXpju4IDYqOgbjZy9G+YZ/YB1zJh05dT7uuN7H\nyYtXYVezKc5dvYkewyfi1MVrWLttH+q36ynEqjz2vmNnUKlxe9Z+CYZNnoM9R07h8bMXGDVtPhp1\n6M2u0yRJQ0izTWT38waOm4GQsDA0b1QXN267oEy9Vnj64pW8rro5ySv9m3nvHwgnJhZV9+ECXlWJ\nC25VXafhwt4m9WsrNeH9HD93GYN7dlEql3a83sjuR5oZG0lFYmtkmF9sX7x+q1Se3s4bH19x3cfM\nOMVwSKrLnWm92LmWrgtJ5bQlAkSACBABIvC1BHJ8bQBqTwSIABEgAkQgKxHgfzRd2nYOY3ZNEsOy\nqWiHKs2ry4foctoZYf6hsChWSDwlWsWhGvbP3oV3T71hU8kOPeb1xeXt58GFmSO2jGXiBy0h5OxR\nqBMOzd+LmecWiLLOU7qie8GOeHTtAeyqFEedzvVx/7IruAi22YCWKFzSUvS5j8U+vHAfru68iMZ9\nHOTjUMxwIW0B5hBbq0NdUdx74QBwF9TtEzZi6ok54g9BdXNSjMXzTocdsX3iptTFSvvcxfVg+Cml\nsvR2PG49Rjbm5tViaBt5lTJ1y+H9Sz88dXoCzlBKXAyrn08fOrl1hZg1B3vNG3fh5edF+iM8NlL2\nehTuMstThUaVxIfn3z5+jaU9FgiuJ5YfQdu/O/Ji5DHOi+aDW4sPFyzvn7Mbx5YcZELZ5VjpvgF6\nzC2XEhEgAkSACBABIkAE1BHg65EjO9ywaLtsfVGqgjnqNismb3LtnCeC/KNQxM5QCBLqNrXD2nnX\n4PUsENxd9O/ZjXFslzu4i+u8DW2FYysXxdYt+g82LLqBzSd7iLLBE+ujtvUCON94jTKVLdC8Q1nc\nvuyFM4ceo3PfqrApYSz6XMNib1rsiOO776NDr5SHjOQDYhnujsodYpu2kzm8jZ3bVDilLp5yAesO\ndxVrLHVzUozF8xeOPcGSKRdTFyvt52DrPtfAqUpl6e243fYWrq9dB8lcWUKDokVVbZ20l5u0dXOy\nB8I+ITw0DvkK6KJmQxvx4Q2eP/HH+D6HcffGG+xYdVuIj8tXK4QcObPBjTn1Kq4loyMTRB8FmVus\nqhQaFCOEvQs2tZMf/tJY8gCUIQJEgAgQASLwHQjw33/bTl7HrtlDRO8Vi1vDwb6CfCQbj15Bw6pl\nxPUWSzMjlLUtzBxVHwi3Vi7inDe0M3acvgFf5uK6edoA4djKRbGFHYZiwfYTOLtygiib0rcNLJoN\nBndrrVKqKDo1rolLdx8zEewdIbyUXEfnbD6KhTtOYueZm+jTur58HIqZ9UcuCYfY9o1k1+IWDPsT\nxZlIdOLqfTi+ZIyIqc+utenryhzVpvdrLxfdKsaR8m3+Xoyo2HhpV+V2Wr92GNu9pcpjmhTevC9z\nmqteRtnpTLGtK3PX5akDm1f35nWQwIQvnOE/O08hJj4BfJ6q0rFrLmjJ3Hila2LcQfZDcDjK2BTG\nhF6tkd9AX7jxNhs+H02ZsNh9zwLBj5/7SUx8vDCduKr6ojIiQASIABEgAj8aAf77bvOew9i3fokY\neqVypdDi93ryaXDh6ofAIBS3KSLu57VoVA8zF6+Bx3MvVC5XGgunjsHW/Ufx7r0/tq+cL0SyXBRr\nWrY25i5fj0sHt4iy6WOGwLi0Pbhba9UKZdGlTXNcuH5LiGAH9+iCksVkD/TOXLwa81ZuxPYDx9Gv\nawf5OBQza7bvZc6zxujYqpkoXjR9LIpWa4xxsxbh9O714vqFujkpxuL5Q6fOYxwT8apLOXLkQMxr\nd3VV5Mdu3nVFjuw5MIKJiaXEOY+fswSLp4+TitJsA4JDkI29ZSBXLtmDyFIFXR3Zms0/MFgqUrsN\nCJI9lKStwj2Xx0piD0qFhIXDML/swWa1weggESACRIAIEAENCaS9G6FhQ6pGBIgAESACRCArEuAX\nkwvaWmBJj/kYtGo4qraoIVxFpbHW6lgXRcoXRV6TfEiMTxTCTH7sPXMx5eJXnrhwkzuYcuGrtJ/f\nrADMbMzlZVrsIn0BC0MEvvUXdfgPXsZFpZLwlZe1Ze6yRxcfECLR9MSvp1YdQ9GKttg4ag1vIpK5\nbUFEhUWJfEZz+reJfOMwqBUa91UttJVX0jDD3Vf3z9mFiYdmQEc/xf2i48S/8OCKO9YPXYnIGT2g\nl0cPj64/gI/HWxSrJnPZMrQwQpfp3bFrylasHrAUNdvVga+njxDn8u4ty1inGYVVmSJYdGslhpbv\nh1uHrsvFr4oVOeO/WJ/52DncMnY9njg+QrVWNRWrUJ4IEAEiQASIABEgAmkI8DWVla0hxvU+hGnL\nW6K+Q3H0GJqyhmjWrgxKlDVDAWN9JMQnC9ElD8LdSrn4lSe93FqwsM4nRK7SvpFpblgWyS8v02Ei\nT9OCeeDnHS7a8B86ernYK+eyyYWvvIy7y25ddhPud7zTFb/uWnsHJcubYx5zjJWSpU0BRIbFid2M\n5iS1kbZd+ldLty+pjqZb7tDKxcEr93aBrr7s9cd8niIx1qnTx4//Q85c2WGQV3bjRPF4sdKm2Hdt\nAFpXXYVzhx8LNqYWeTB0cgMsn3EZ04acQOM/SuHNiyCcP/pENLVjbVSlK6efoSwTHfPzKKUvjSW1\npy0RIAJEgAgQge9BgP+ety1shu7T1mLVuF5oUbsiRnSRCS34eM6tmghdbdnvXu6sykWuUTGyNYI0\n3tx6OrAuaCxErryMi2LNDPOiqIWJvEyXvbnHwjg/vD+kiBr0WFkO9uphSfjK247u2hyLd5+G04Pn\n6YpfV+2/gIrFrTB6acpbmGwLmSIsMoaHgJ2lGW7x9rM2MMFoF1iZG4nxiIMqfrw6uVJFqXJRTjbO\nL03ciXbj0cvYNn2Q2hAPXngLHn82sRf1tJgwZGrftjjl6Cacc6f3by/nKQXiQpMT112xhQmPpfTg\nxVuR5eeSC195si1sigVDuwhH2c3Hr4KLeVcfuCCEtsb584g69IMIEAEiQASIwM9IgK917Ipa4a/B\nY7F2wTS0atIAowf0lE+1U+tmKF+6BEyMCiCePWzi6Owqjnm98RbiV75joK+HIpaFhMiV7+dm++Ym\nRrCxLiwv09XRQSFzU7x9J3Oi5/X02JqIi0ol4SsvG8vcZReu2YKbd93SFb+u2LQTlcqWwvDJc3kT\nkeyKWCE0XPZ2mozmJLWRtkN6/Yn+3WQPaUtlX7rl9/O4OPjo1pXQ19OVh1mxaRc4S84xvaSvm1Jf\nsY70hkYT4/TbKtaX+lVxWUi86YeLa/PlMVBsQnkiQASIABEgAl9NgMSvX42QAhABIkAEiEBWI9B3\n6SAs7joPCzvPZq8bKYeRW8YJsSsfJ39yMY9xPnBH1lzsVarcGZan/31i72dVk3JoKT/tyKtyV9P4\nDNwnhEi2oCEigmV/+KbuIiY8WjjRNurZVMlBNXU9dXNKXZeLQ/knM9KOSZvRalhbFClXVCkcFw8v\nurUKN/ZdEW6tlqWt0aDb77iw6QxK1ykrr/vHyPawrVRMCGU9b3vAvn1dvHDxxIdXfmliSo04My5a\n5m656pI9E9NuHbcB71ksSkSACBABIkAEiAAR0ITAhH8cMLbnQYzqegBV61hj/sa2cpFktmy/iTx3\nZNXSzgHuDMvTpwzWiTm10q67uGNpXGyi2iFxkayJuQHCgmNV1ouMiGdOtNFo270i6jZNcahNXVnd\nnFLX5QJc/smMtHTqRXQbUgPFmWBYSlz0y5OqucdGJ4ALd7NnV90/51G/WXEc35PyOr+ew+1RulJB\n3Ln6Sri5cgfcR66+QpBcvIxq8eulE0/RqFVJaUjy7ZfEkjemDBEgAkSACBCB70Rgyaiu6DZ1DbpM\nWom6lUpiKxNSSoJIc6N8uHLvCc4xt9da5YvBmglJHzx/m+FItXKlvS2UkzuKMVGJusRFsgWN8iM4\nXPawduq6XEjqHxKOHi3rKjnUKtarW7EEhnduipX7z+Psrfv4Z8Rf6Na8tmIVpbyO1r8P1iiVZt7O\nhFX7mFjXGmecUtYfr3wDhLPriRuuyKuvK7gbMBFxHvZQeA6F6238GmPlkkXx3PsD3vgFomQRC6WB\n3Xn8EknJybAvl7KOM/hXiGKY6g1GVUvbiLYvWKyXPv44ft0Fw5nQmY+Bpzj2AD9Pj176iLJqpWxg\nykTMlIgAESACRIAI/OgEVsyehC4D/0aHfiNR374adqxcIBdp8t+1XLDJHVm1tLTkgteMrtNo5Uq7\nfhBrHeaAry5xkayFmQmCQ0NVVguPiMSHgCD06txWyaE2dWV1c0pdlwtw+SczEnd3HdGvuxAMS/Fe\nvH6Lo2cvYtSAnjh27rIojouTueo/8PAUZdUrloOFuQm7/vUJCQmJjHUKv6ho2TWrErbK9wil+Km3\nFkxkzFOsCtZRMTGwtbYSLr6iEv0gAkSACBABIpBJBDLnN2kmDYbCEAEiQASIABHIDALWZYtisdMq\n7Jq2DZe2nMMY+2FYdm8dcufPjQDm1Dqt6Xj0WzYYlZtVw/uXvhp1qeopRd6QP8WpLiUlJCE8IAzl\nG1VSWe039sc7Tz4eb9SKX9XNKXVgL7cXeHgt5aJ96uN8PxsTHbQZpfq1LVL9i1vPCYFqlebVpSKl\nLXd7dRjYSl62jrnA5jcvgJZMLKuYStUuA/7hifN3PXMX3ef2EQ67ivUU8wXtLJjTrsxhTbFcMW9g\nlAf67JyaZ1BPsQ3liQARIAJEgAgQgV+bABdM7r8+ACtmXsbh7W7oXG8DDjsNRp58OsypNQx9Wm7H\npEXNUaeJHby9ZK9qy4hYeuvB9MqleIkJyQgOjEbNhjZSkdI227/rzJdPA9WKX9XNSSkg23ni7oe7\nN16nLlba5zeXeo2QuZopHVDY4ey46LVesxQxBz9sWtAA2kzEGuAbqVBblg0LiVUSyqapwAqs7Axh\nWVTZTaSyvRX4hyd+jq6fe47RsxoLF15RqPCD9+Hm9BazVrdWKE3Jfk6slFaUIwJEgAgQASLw/QiU\ntbXEra0zMX39IWw5cQ32vafj7s45wjV09qYjwkX1+NIxwnVUEkpmNNrfoPpaVgaXuIQgNCA0Ao2q\nllbZBV9D8OTxyjdd8SuvM3dIZzRkMf5etguDF2xBUHgkRv/VXGXMVUwkm8Bej6suceFv9TK26qqk\ne4wLea+6yFzlpUqRzD03lolNxy7fLZxvueiYu9fevO+JdwEhKGSSslbhrro86bMHuVOn49dc0LxW\nRaUHf2wLmYhq91OJlHlM7rTL47wPChX98P6lxExkRTp69R7OM7Hz2gl9SPwqwaEtESACRIAI/NAE\nypUqjrvnDmLy/OXYtOcQqjl0hPulo8ifNw/e+Pji9469sWLOZDRvxIxVmJBTk5Te9Zj0yqWYXPjp\nHxSM3+vWlIqUttJa54nnS7XiV3VzUgrIdlwfPsGVm86pi5X2+UPEYwb1VipLvbN5z2GUZyxbNq6v\ndMjvQwDe+flj9LQF8vL/QbawOHz6As5dccSGxTNR3KaIOP7ugz9srArL64aEhYm8puLXQmam4CLi\nd+/95TGkTEhoOMqVLi7t0pYIEAEiQASIQKYRIPFrpqGkQESACBABIpAVCHCxqdNRR9Tr0hD9lw0B\nF27O+WMq7p50AndXPTB3D5LZRXMufOUpoydEv3ZOz+8+Ax9T5WZVVYbSNdCFsaUJzjPH1BZD20BL\nR0te78a+qyhZqzTyMqdadXOSN/g38/6lH+4cu5W6WGmfO8OqE7/ePXmb2eH+D/X+bKTUzuPmY7mQ\nVfEAr395+3mM3jEB2nraiofk+aTEJCzpPh/mTNjatH8LebmqzN1Td5j7q2rRrVT/GXOS/R97ErVE\njVJSEW2JABEgAkSACBABIpAuAS42vXjcAy06lcOkxc2FcHNwhz24cuqZcFddt+A6Wyd+EsJXHuS/\nXic+dPFFYsJHeX+pB65voAXzwnlxcKsLug6qDm2dlDcRnD74CJVqWqKAkZ7aOaWO6f0qBNwZVV3i\nzrDqxK9XTj9jy8T/oWXnckphXJnolItL23StgJsXXwp+3E2Xp+jIBPi8CsWIacprS6UAbOcqi13P\nQVlQK9VJSvyIcb0Pw8rWEB37VJGKlba8fYlyZjC1yKNUnnpHk1ip29A+ESACRIAIEIFvTSCBXUfh\nYscuTe2xdHR3IShtM2YJTt5wQz0myPxn5ymsGNtTCF/52P7rtcs9Dy8hgG1as7xKFNwd1dLMEJuP\nX8XQTk3k4+KV91+4DXsmUuVC024OtdGgSmk4bZ2FjhNWYP3hS+mKX0/fdM/QkdY4v8EXi18P/zMq\nzVymrD2Aveed8OLYcvmxP5vVwtaT1+Hi8UpJ/Or51g/cgVdREMsb8bUSd29dPV5ZqGJSIK8Q/vI4\nisnrXQCS2auKuYiXi20V++b1Ypkrr8nvAzBjQHv0/aOBYlPKEwEiQASIABH4YQlwsSkXYP7VriVW\nzp2MFo3roWW3QTjOHEp7d2mH2cvWCRd1Lnzl6b9e6zi7PxTOpw4NZf2lBmuQWx9WhQpi466DzGG1\nG3S0U+6F7T16GrWqVYKJYQG1c0od8+Vrb+bMeil1sdI+f0BGnfj1+PkrYu3RtX2KWQ0P4OjsKtx0\n37jIHF+loLFxcchXrBrmjB+B/t06iuKyJYph3soNuONyX0n86v7oKcqWLAa7IpZSc7Vb7hrbq3Mb\nnLvqyM7XJ/E2Tt4gMioaL994Y/aEEWrb00EiQASIABEgAl9CIMeXNKI2RIAIEAEiQASyKgF+cfni\n5rOo27mBcGUt37AiDAwNkLuAgRhyQmy8cGJ1u+AC20p2THR6WpSH+ocgJjwauszNlNfhglXFFB8d\nj+hQ5de6xcfEI/Hf145JdT8mf4Svpw8sisuejHQ+4SQErJLYlteLjYhhfSSIP0b5k6atR7bHplFr\nMN1hArrO7CXGcO/UbeQxygujQsaiD3VzkvqWtnU61wf/fGl6ePU+ji09JGKcXX9ShPn08ZOYV+GS\nVmnEr1yEyl12ufDVvl0dld1yVhtHroGJlSn6Lh4ELr7liTvvcuFvvb8aCZdZXubz1BsJrH778V34\nrkgnVhwRotp6fzaEFnPBEOd5y1kMXDWcnV/14gYpBm2JABEgAkSACBCBX5sAWybi0DZXNO9YVqwT\nazQoinwFdJGXfXiKi01CcEC0EG6WrlQQB7a4iPKgD1GIjIhHbiZGjYtJZOvEj6Jc+sHLIsKVX53H\nYyXGK7uUJSd/wuvnQShSzEg0vXLyqRCwcpdZKUVHxrM+kuTrxJ7Da2LemLPo13oHhjPhKB/D1TOe\nyM9Er2ZM4JnA+lA3JymutG3eoSz450uT8/XX2LbCSTDct+meCMPXiXxeNiWMhfi125AaOMPEuZfZ\n/Br/IXtI6cKxJ2jQvDgatiwh2nBXXc63VZdycjdYr2eB4hz0G5N2PckZzx1zBgUt82LCQgf2SsBs\nKqfAhb0NW5ZUeUwq1DSWVJ+2RIAIEAEiQAS+FwF+7YO7vXZuUlOsXbhbqmHe3CiQRx8x/76u9vBl\nZ7RvWA2PvXzg9PA5EhOTEc2ua3FHL30dbcTGJbAy5Wtc0axtWGSM0rRimLgyIdW1MC7G9Hz7HsWt\nzEXd49ddwV1Wm9nLxK/cIZWnGNaHlEZ0ccDopTvRfPhCzBzYHgZ6uuACVqN8uYVA9JVvABPAeqBR\ntTLQ1dZCy9oVsZ25r6aXLqyZlN6hzyoPj5LNlwuKvyRVK22DP5kIeffZm2hTv4o4H8nsGuDthy8w\na2AHsa8Y9+4TL8GFi5RTp/lDu6D+gFlwfvxSLtp1vP8MxSzN0JWJbCkRASJABIgAEfhVCPC1zsbd\nB/Fn2xbid+nvdWrCMH8+FGAfnmJj4+AfGMyElDdRpXxpbNi5X5S/DwhEeEQk8hjkRgyrk5iYKMql\nH9ExsQgLj5B2xZbXS0hIWbPwwuTkZDx7+RolbIuIOseYCLU2E7BKYlteGBEVxX6nx8mv04we0BPD\np8xF4059MYcJOfMwQezJC1dhZJgfhQuaIZ6tqdTNSXSk8KNLm+bgny9N3DV28dqtguHa7ftEmI9s\nDcfnVaqYDepUr6xRaFNjQwzu0QVLNmwHF9Hye5d8Lmcu38Cu1QvlIlYpWBjjz1M8EzCnTiP6dQcX\nAx87exntWjQWhw+dOo/WTRqgTTP1D0WnjkX7RIAIEAEiQAQ0IUDiV00oUR0iQASIABH4oQgEvPXH\nsl4LUaN1LQT6BKBJ3+ao1rKmmEOr4W3xyv0l/ukyGxWbVEGfRQPx3PkZji05BJ3cuogKiUR0WDQ8\n73jg1uEbqNS0Kk4sP4zQDyGIjYoFF4M27NEEZ9edRIhfMOKi43B972W5Qyp/7QkXc+bSzoVgvyAh\n4px0aIbomwtluYiVi0V5/sDc3WjWvyUbnwNCfINwnPXDBbDZ2CtMWo9ohyb9Uv7gVTenzDw5rx94\nYWHnWUKc+9L1uVLonFo5senlblHGL0p4ub3ARSZA5U66868skQuMFRtxnvfOOOPKjgtiTtVayc6D\nVIeLYq/tvoQza0+gdJ2ysGGCZP38uTHz3ALkyJmyTPF+/AY39l/F3pk7ULtjfWTPmR0Og1rBrgq9\nIkViSVsiQASIABEgAkQgYwJ+3uGY0O8IGjGB5HufcHToXVmIMnnL7ky0+fT+e4zufgC1f7fFuPlN\n8fDeO2xdfgt6uXMhPDQOkeHxuO/sgwtHn6B2YztsX+WEQCaOjY5KABeDctfTvRvuIsAvkq0dE3Bq\n/0O5Qyp3QT3IBJ9azMHV3y9CiFxX7pM97COJWN3v+AhBK3eh7dy3Kjr0qgx/30jsYP30a7WDvTb3\nN3QfVhMde6c4n6qbU8ZENK/x7OEHjOy6H/FM2PvEzU+pYS6t7Lj09G9RZl4oL7ae6YX5Y9m6l7Xh\nQl1/3wjhtis1imVi1pP7HghWVWpZgYuNDfLqYPPJHsjJ1nlSCg+NxfWzz3Fstzu6D62Jhi1k4lnp\nuOKW13W5+QaTl6SsoVMf1zSWYjvKEwEiQASIABH4ngTefghCr5nr0bpuZfj4BwvXz5Z1KokhcQfV\nvRecULvPdAzv0gyLR3ZFb1a308QVWDexD9YeuoQwJvq8w0SWR67cRZMa5bBi3zl8CA5HFLses+HI\nZXRvUQfrmPOqX2CoEM1yx1Mu8uSJr102HbsiHFx92XEupD24cKQ45vr0NeZvOy7ye87dgk0hUzSu\nXpaNrz6LFYLlrB8HJoDlr+kd0bmZ3K1UK2dOjF+5F/3bNkR+A314MTHs+kl9RZz/6sdF50fgY+Tp\nlKMbKha3RrOa5cBdWD8nrZ3QBzM2HkbPGetQs6ydEBuP79kanRorX+viMY9dcxEi4VwK17akvkpY\nF8TltVMwYfU+1GBOr7wOd9U9vXw8e8AnZR0k1actESACRIAIEIGfmcDbd37oNnQ82jg0grfvewxg\nTqRcJMnTyP494PbIAx37j0Sz+rWxZOYE3HF7iEVM7Gmgp4fg8HBwEaYTcys9dPI8mjWsg6Xrt4GL\nYyOjo8HFoNyFdPXWvfD9EMDWP7HYffikEHfy+Px+HhfUcgfXd+/92VonDse2reaH5CLWW3fdmcAz\nAbOXrsXAHp2FU6rvB38sWb+dCWD7sLVOdowe0IONu5Nox3+om5O8UiZk7j9+ivZ9R4hxuzx4rBSR\nO7C+dbmiVJbRzoIpf7O1SA607T0MjZgQ2T8wCBOH90eFMsoP85y/dhO7GEeeuPC3crlS4G65XEDL\nk6WFOa4e3i5Ewu5sjMZMGMz5rpw7RRynH0SACBABIkAEMpvAb0y8wrxPKBEBIkAEfk0CXl5esLW1\nxeLbq2BdtuivCeEnnDV3X+Wv0wgPCBPOqamnyI8lxiUKJ1F+jP8q5ALOnLlSXiWbuo0m++uHr8LV\nnRdxMPwUgpmYVddAj310NWkq6iSwmwgBb/yZO6qJcDdVbJjRnBTrfos8d7eNCI6ATUXbNGNV7P8u\nc7C1LG0NU2szxWKlPHfZDXoXyOJooYC57I9jpQr/7kQEspszoZEwZu6xXFxM6eclMM5+ODo6dMC8\nefN+3knSzIgAEfjmBPbs2YNevXrAJYAutH5z+FmoQ+6++r9P/0NwYLRwTk09NP4KvYS4JOjoydYa\nsnXiJ7ZO/DohwpzRp3F89324Bk4VQlB9A23oMxdXTVM8G5Pv2zDmfJoPOrrKa9aM5qRpH/9FvbCQ\nWDFPRUGr1E9iQjI+MFGsNhMDm5jL3tIgHZO23OXWrpQJLKxkri9Suaotd3R9/y4CRYvLnHVT1/mc\nWKnb0v7PTeCe4xv0/2MngoODUaBAgZ97sjQ7IvCVBGTrqZ4Ivbr5KyNRc00JcHfRT+y6VUBohHBO\nTd0uirmY5dbVkRdzZ1Otr7y+xYONWLQdO8/cRNj1LfANCIGBvi4TmaT0I+8wnUwccwF7+z4IlmaG\nwuFVqsbnwwWeQWGRQvSZh8X90VIiu4b4jjGxNjdK44ImzYXPPTfjxV161aUPwWHQZuKUfLn11FWj\nY9+RgGWLYZi74B8MHDjwO46CuiYCvw6B1q1aQTf7R+xYueDXmfQvPlPuvsqvxfgHBQvn1NQ4+P28\nuPh46OnK1gz8Ok0S+12c6yvXO0MmzsL2A8cR89pdCDO5g6sB+2ia+JjeePvCqnBB6Ooor5EympOm\nfXyvetw5Njg0HCZGX//3cXBomHDHzckegKL06xG45nQXTbv0o+stv96ppxkTgW9N4HyKpdq37pr6\nIwJEgAgQASLwHxHIzi6i8/+MChmr7IE/zamtpy0/xl/f8bXCV3mwfzOGFqpvuqeup7ivpaOFwiUt\nFYvk+YzmJK/4jTIWxQvDQoO+JMdddVW5o6y5TUF1VcSxPMZ5wT+UiAARIAJEgAgQASLwpQRy5Mgm\nmppZ5FEZgjucScJXXkG2Tvw64WvqjkzT6Tt1PcV9LhC1KaF6bZvRnBTjfOt8vgLpC1pyaeWAZVH1\nN1IaNNfc5Z+ft/SEr3zenxPrW3Oi/ogAESACRIAIpEdAcgItZKL6d6ai8JXHyAzha+qxWKTTd+p6\nivs6TNDJHU5TJ2k+RvlUP/iSun5W3OdOrUUtTNQOzYoJYzVJZoYZP+CjSRyqQwSIABEgAkTgRyXA\nnUZ5KlxQtYEKv58nCV95PX6d5muFrzyOYipkbqq4q1Geu8WWLGajsm5Gc1LZKAsVcjfbzBC+8ikZ\n5qe1ThY6tTQUIkAEiMBPS0B21+ennR5NjAgQASJABIjAtyOQGJsA7tAaFx337TqlnogAESACRIAI\nEAEiQASyPIG42CRwh9bY6MQsP1YaIBEgAkSACBABIkAEYplzazJz/YqOjScYRIAIEAEiQASIABH4\n6QjExsWz6zTJiI6J/enmRhMiAkSACBABIvCrESDx6692xmm+RIAIEAEi8J8QcNx/DQ+uuovYu6Zu\nxZtHr/6TfigoESACRIAIEAEiQASIwI9F4MyhR7hzTbY2XD7jEjwf+/9YE6DREgEiQASIABEgAr8U\ngQMXb+PKvSdiztPWH8Sjl96/1PxpskSACBABIkAEiMDPTWDfsTO47HhbTHLSvGV46OH5c0+YZkcE\niAARIAJE4CcnIPOR/8knSdMjAkSACBABIvBfE6jUrCoqNa0i7yaHVk55njJEgAgQASJABIgAESAC\nvy6BOk3sULuxnRxArlzZ5XnKEAEiQASIABEgAkQgqxFoWrM8mtQoJx+WVi66xiWHQRkiQASIABEg\nAkTghyfg0LAOmjWoLZ+HllYueZ4yRIAIEAEiQASIwI9HgMSvP945oxETASJABIhAFiSgl0cvC46K\nhkQEiAARIAJEgAgQASLwvQnkNtD+3kOg/okAESACRIAIEAEioDGBPPq6GtelikSACBABIkAEiAAR\n+NEI5DHI/aMNmcZLBIgAESACRIAIqCFA4lc1cOgQESACROBbEwh6Fwi38y54ff8lBq8d+a27/+z+\nXM7eRXx0nLxd9T/s8enjJ9w7dUdeppjR1tNGlebVFYs0yns6P8XDK+7InjMHyjWoANvKxdK0e3z9\nAdwvuiKvST7U6lAXBcwN09SRCsICQuH33Bel65SVipS2AW/9cf+SG3Lp5EKlxlWQxziv0vHP3Umv\nv4S4BI1ZacIgJjwal3dcQLBvEHOhrYoy9cohe3b1zmJORxxhbGmikqmm80xvflJ7j1uPwcevpaPF\nmJeDVRlr6ZB8q8n84th37fbRmwj0DoBd1eLiu5CDfSe+JL1w8QQfV7Zs2VDjj1qCgao4bufvITYy\nVn4o2C8IDgNaQkv380QsmvSn6fnThKc04Hun76B8o0rIpf3lTy6r+/fgcsYZ8THxUneo0aYWvvSc\nyINQhggQASLwExFISvoI99vecLzwAtXrFWXun7ZZenY3zj9HbHSifIyNWpVEThUupeGhsTiyww19\nRqW4ZEiN/LzD4HTFC1raOVH7d1vkN0r7gNA9xze4eekljEz00aRtaZiYG0jNlbaOF18gJjJBXubv\nF4nO/apCR1e1+9iFYx4wL5wXZSoVlLf53ExwQDTevAxGlVpWGTZN3V98XBKunVH9qjwdvVyo1yzt\nGpZ3oo6nNAge+/rZ5wjyj4KlTQFwN1kpPbj7DneuvWK/g7OJ79mXzP9zxv7Y1ReuTt5snfkbGrLv\nSEHGXF3SZH7XznqiZgMb9r35srVdZEQ8ju9yxwffCOGyW62uNRtfNnXDyvCYOua88ed8P9XNz439\nP+LBXR9o6+REldrWsCtlkuHY0qug7t/W55xjxfianL+MWCnGSy//OTx5jNT//q6fe464GIX/f7Vm\n///Kqf5vofTGQuVEgAhkLQJJyclwevAc524/RIMqpZQcSbPWSGWjOet0HzGxKeuX1vUqI9e/1y5C\nIqJx5pY7fANCUKpoITSsUhr6GVxfOHr1HgqbGqJyySJK03XxeIVbDzzF77vWdSvD0sxI6bi0c/72\nA0Qp/N3uGxiCAe0aQVdbS6oi356+6Y5GVUtDOxPcz/hct528jjHdWsjjxyUk4rSju3xfMaPLrhk1\nr1VBXnTD7Sku3HkEU8M8aN+wOsyN8smPSZmMeH5Of1JMdduExCTG/DkevfRGjbJ2qFqqqLiupK5N\nesfevg/CpbuPocPeHMVddo3yqV4Pp9desTyzYmXm/BTHl953mNfJ7O8nP+dn2Pf4Q3A4bAqZopl9\nebx5HwhXj9fyIdlZmqGcnaV8nzJEgAgQgaxAwMfvA85dcYT746fYsGhmVhiS2jGcvnSdrXdS7pu0\nafY7cqVyij954Soa17WHtoo1R3hEJLYdOIZ3bN7NGtRBg1rVVN7LeuPji4vXnaDDYjRlTq3GhgXS\njOua012cv3oTpiZG6NiqKQqapv2bOio6BvuPn8Xbd34oalUInf9wgK6OTppYn1Ogbn48jn9gMJ6/\neoO6NVLeGMnL4+LjwduqSnxMLRvXVzp0ln0voqKj5WXv3vtjcM8unz1+TVjKO1GTSWC/ax2dXfHw\nqSfsq1REtYplv3g9lJmxMmt+IWHhOHXxmvhuli5hh9/r1IS+XtoHw+7dfyQ48HuwbZo1glWhtNci\nNZlfRv8W+Fhi41Luw7d1+J1d61B9XVTNaaNDRIAIEIGfnsCX3VX46bHQBIkAESAC354AF/Z53nmK\nwwv34bfffvv2A/iCHrdP2Ij8ZgUwZP0oJgbUEqK3G0euYlW/JSqjVW5W9bPFr1vGrsf1PZeha6An\nRJ37Zu1E19m90GZUB3kfx5Yewo39V1G8Wgk47r+GXVO2YuKh6UIAKq/EMhFBETi+7BDObzyNRr2a\nqhS/8lj3L7li4Mphov7UZuNFvqR9acVQGuUz6u/O8VsasdKEQVRoFMbXHcEYlETIhxCcW38KRSva\nYuGN5emO1cv9BVb0WYQ+iwd+kfg1o/nxjjeNXotEJvLtu2QQgt4F4Z8/56Bpv+ZwGNhKPi5N5uf3\nwhfz2k9H70UDUbNtbbgy4fWQMn0wfPMYlKpVRh5Lk8w29r2NCApHt1m9ERcVi51TtuB///sfxuya\npPRvz/f5O9bnDKWQ9u3rfLbwVZP+ND1/mvDkA+ai3f1zduP1Ay/seHfgi8WvGf17KFLBBknxSTgw\nb7f4t8dF1yR+VfrK0A4RIAK/OAGvp4G4eNyDCUXdUbS4cZansXjyBRiZ5sasNX8IIR4XU6pKM4ef\nxEMX3zTi163Lb+E2E75OXdYSocEx6NNyO8u3QMUaKTeXeZ0zhx6hfNVC2MG2y6Zfwop9XVCncYqY\nk/f55kUwhnfep9R9k7al0hW+etx/j8kDjmL8gmZfJH7l4922wgkHt7igbfeKGYpfVfV3+eRTTBl0\nXGnM0g4Xq6Ynfk2Pp9T2KhPUrpt/DX8Nqi4+2bKl/K2wcMI5nNr/EPoGWvD3jcSaudcwYnoj9Bph\nLzXXaKvp2Pl3JDQoRvQRE52A5ez88XXUom0dlNZRip2qmx8XPK6bfx3PHn6A4+txXyR+jQiLw18N\nN6Ec+04FfojE/k33UKqCOXZf7qc4jM/Kq2POA2n6/cxofvPHnkU8W0tNWOjAzl8ERnc/gI59qqAL\nE3l/bsro35am5zh1v+rOH6+bEavU8VTta8pTaqvq31+JcmZITEjG+gXX2f9jHgsRNIlfJWK0JQI/\nNgGPV744es1FCClLWKe9qZzVZjdx1T6YGebFukl9octEpDlzyIT4XDDZd/ZGrB7fi4k5q2HDkcuY\nv+04ji8ewwSeqh8kcfd8gz6zNmDRyL+UxK8TVu1FUFgUZg3sgKjYeExdd1D8Pt45a4jS7+Pn3u/R\nYbzydaF2rO/UwlcuQJy79TgePH8Ln7NrMkX8OnThVtx94qUkfj3OzmP/uZtUnrJmNcvLxa9L95zB\n/gu3Ub2MLVsn3sGUtQdxcMEINGV1pKQJT037k2Kq2waFRaL+gFlsPi3RrXkdLN97Fot3nRbj4g9W\nf07i87vk/Bgrx/ZEUHgkmg2bjxUsb19O9YNS6mJnVqzMnJ/ieNP7DvM6mf39POXoxr7HxzCkQxMM\n6dhYLsQxymuAamVs4BsYiubDFzLxd0MSvyqeJMoTASLw3QlEx8Tijut9zF+1Eb+x/36ENG7WIpiZ\nGGPTktlMhKnNxHgpkhMu1py1dC3uMyGv/6NbacSvoeERsG/ZBdUrlcd7/0Cs3b4PlcqWgtOpvUpT\nX7R2KxO+3sKaBdMQFByK3zv2xpr501CrWiV5PV5n79HTqFG5PPZt2I6Jc5fi6NZVcGhYR16HC1B5\n29x6evD2e4+kpGQsWrsF147shKlx+iY68gCpMhnNLygkFIvZuNbvPIA+f7ZLI349euYSeo+anCqq\nbLd5o7pK4ldPrzdo02uoUt0OLZt+tvBVE5ZKnaSzExgcgtqtu2L80L7o2akNlqzbhoWrNwnmn7se\nysxYmTW/hx6e6DliEtb/M10Iqfl3c86ydTi9az37vqc8aDaWff/5+OdOHMmEybGYNG+ZWIvvXbdY\nvhbXZH6a/FuoWLYku26UiNnL1mLfsTNCLE7i13S+oFRMBIjAL00gZSXyS2OgyRMBIkAEvj8BHX0d\n1O5YD7eP3YSX64vvPyANR2BdvihMrc3ktbnr64wz82FTyQ45cqX8mpnZYhKqM4fNz0nOJ5zwG7u5\nv52J9/gfTo+vP8SS7vOwd8YO4dbJ+/V/8wFGhY2x/N46EbrH/Dj0t+uG02uOpxG/BvkEoG6Xhji5\n8qjKYXDR657p2/HPrZUwt7UQn1bD2mBhl9lYemcNChT8vD+EM+pPE1aaMOCTuX3UkQldVyB3ftnr\nWg4t2CvEj8/ueKBEjVJp5svdOg/M3YOPyR/THNO0IKP58bFf3n4eW17tEYJRi2KF0HN+X8xtOx1F\nytugePWS0HR+28ZvFCLXSk1kT8nyfysPLruBi6HnXFyk6ZDx0vU5Tq8+jg2eO+Tns9vs3hhcujee\n3HjI3HJTbqScWnUMM88ugIm1qYjPRekGzHHkc5Km/Wly/jThycfGHaQLl7Ji39+CQvz6OeNVrKvJ\nvwfJYbls/QpC/KrYnvJEgAgQASIAcDFWp75Vhfj1R+HBx2xhldZZSxo/d3x95Rkk7cq3Tpe9sGr2\nFey71l84k3J30m6Da2BU1wM46DgQJgUN4Ps2TLiEHnEaLNr9PbsJGpdeij3rnNOIX3etvYNNJ3vI\nx8Jv/+QzTOsiywNxt8f1C68jOfmTiPslP977hKNl53LYteZOhs3T64+LADee6I7SFQoqOeb2/2Mn\nGrUqoTJuejylykunXcSBzS5MyNkXtiWV3UuunHrG1si/4carcWLLXT/H9jqE1XOu4HfmeqnuPErx\npa0mY3/s5ofd7FydfzRSnE/eduSMRmheYSVcbr5F1TrWUjj5Vt38uEsrnxP/rnDx65emi8z3BNJc\nAABAAElEQVTxd8+VfsiTT+basnHRDaxlgtr7zj6oUL3wZ4dVx1wKpsn3M6P58fN3lLnVXvH8W4i6\nre0M8ffsxhjaaS9KlDVD+WqFpO4y3Gryb0uTc5y6I3Xnj9fVhFXqmKr2NeEptUvv35/kIF2tXhEh\nfpXq05YIEIEfn0D5Ylbo36ahEL/+KLMpZ2cFa/OUB58+ffqEAXM3C4fPqqVsxDRG/dUcJ264CjHo\nyWVj00wthj1IPI8JUpM/Kl+3cX36GmsOXsSzw0tQ0Di/aDdrYEeU6TQWju7PULdSSXms1Qcu4OzK\n8bD6dyx8PWWYV/k1v+/+daG1KWQixK/yxl+R4Y6vz974pYnAnWXPrBiPisWt5W64vFLLkf+AO+Ty\nxF06LZnT7b2dc8X+vCGdUaztKKw5dFEuftWUpyb9iU4y+MH7+2vyKpQqYoGeLeuK2jMHdBDMZ2w8\nzETIHTOIkHL4kvMjzNhwGDc3z4BtYVPxGdqpKf6ctBK3t82Wn9OUFunnMitWZs5PcbTpfYelOpn5\n/Zy8Zj82Hr2C6xunCVdlqQ++5e7K/MMdlFU5CCvWpTwRIAJE4HsQ4K6SnVo74Mjpi3B58OR7DOGL\n+qxQpgSKWFooteUOtqWL28LW2lKIX5UO/rtz+NQFJnTdh/x5Zfda5q7YgFlL1uC2y33UrCJzgb/A\nRK9TF66A85n9sCtiJT4j+nVHh34j4XLhECzMTPHa2xdWFua4f1l2v++fqWNgXbURVm3ZrSR+HTtz\nEc7s3oAyzMWTC1OnLlyJbfuPYto/K7Fx8SxVQ0y3TJP5efu+R9f2rbB8006Vcbjr64X9m1G5XGm2\nHkpx8Gz6Zz/hIKrYaAWLcfHAFlgXlnHm96iM8qd/zU6xrZTXhKVUV92Wrxc6DRgtzm/vLu1E1TkT\nmCFPLQfBlAtBNU2ZGSsz59dn9BQmLq3N3GzLiamMGdQbx85eRp/Rk3F2z0ZR5vKAPcC0eRe8nC+K\n7yEv5HPnHK7fvof69tWg6fw0+bcgORk3rFVdiF/FIOgHESACRIAIpCHweY+kpmlOBUSACBABIpDZ\nBLJzRwh+NfoHTEns9V9t/2YX3OuWAxfz5mSvOeGfmLBoITqs4lDts2b1/O4z9JjXV7zuhP9RV7Z+\nedi3q4NPHz/By00mEP7IXidcq73swjMPzvut1rImdHKnfQ0FF+QWLKb8x7jigI4uOQjrckVRhH2k\nVKdzA8QzV97LOy5IRRpv1fWnKStNGPBY/NX2kvCVD7Dunw3FOHVVcOAHdk/fhvbjOos6X/pD3fx4\nzAtbzsLY0gT6+VJurNhWkrlYHF18UHSryfx4xTD/UPg88xZtpB852avhkhKSpF2NtqHMFZend54+\n8vo8Dk+co5TCAkLh/eQNzIqaw6iQsfgYWvyfveuAi+J428/fSlEsWEFFFLti77333jWWGFtiTWKN\nxt5b7BqNvXdFRcUugiBipYigoCJVBKmCmnzzzrnH3XFlgdNovnl/v2Vnp7zzzrO73Nzes88UTLeC\nqpz+5J4/OXhS/FK8hUqoE2SkscndG/t+kNuvqCcQEAgIBP5rCEhLr38jwv564X8eEIXHj8JAKqaa\nRqqT5RlhjzbJOvaxRyIjpp7Ye5dnfWDztrY9UtX0zXLlQIuO5WGeO6fUhO9fh8fjiXc4StjmR9Fi\nefhWhO1zmqS+WKXaYM28yxj+S2PVrHSnK9ewhm2ZArLaaevvfcpHDJvYCHXYsvU0ruw5svItNiYJ\nXndfaVV91YcnBUJkxd3rb2HK4nZpiK9U/sDjJX5hZEm6xmiuXLdpKY7vx4//wJv1Kdfkxh4ZFsdd\nPvNLJT9n//SyGyluapqh8Unn1qp4Xs2mso8p9gYtSyuJr9SwEyMxk5EabnrNEObkT+71aWh8R3bc\ngVWJvLDIm7rUYuWa1jzkbX84pyt0Q/eW3HOs2qmh8ycHK1V/utJy8ZTaa7v/pDKxFwgIBP67CHzr\n86nb3k/h9fQlqpZRfymjZoVSuHrHG/f8gtKcvNl/HsHkwZ3T5Ie+juZ5j4NClGU5P30eJzMlM8nC\no2J4n6WsC6N4YUu+FWN7E6ZGq2pSGRFOjWH+L8JAqqztGig+jyWfKSy2X77riCY1KnAiYg6mEEdb\nTFwC7vg+Q4dGCrLLe/aSNqnTSkakxc5NasLCLPXzUg6ecvuT+tG3d3ngh1uP/BnxtZmyGl2TA9s3\n4gq+RPKUa6TUWrWsjZryaL829RHPfOw6c0OuG17PWL6MOT7VAei6hqmOMa9PUnxde/A8lk0YmIb4\nqhqPSAsEBAICga8dgWzZsilVI7/2WHXFV8K6KGgrWdxKa5UU9htM66YNlMRXqvRdT8V8xyJ36gvP\nyzdsQ7XKFfgmORrQvRPiExMZcfUEz3r/4T16d2knFfOl6bu2awmLXKl+7j70Qf/uHTnxlSoWtMyP\n2b8qlPLdPO8r28pNGBof+SFSa7nStlpd0vgnj/kBzRrU4fHmYL+f0hb9NpYRnx+hU+vmynZhEa/x\nyPcJSpcszjGlvotbFUmjpKtsoCMhB0sdTdWynd09OUFZIr5SYdasWTGIEX1JITWBnRu5Zkxfxhqf\n+92HHO9qlcurDaN2tcq47OwGupbIQsIVz8R8/Z8q6+XMoZhfJ6ek8Dw545N7Lyg7EQmBgEBAICAQ\n0IuAIL/qhUcUCgQEAgIBeQg8YoqRtDw4baR0KZnXjYc878oeJykLyexh5l0nDxxddhBELosKea0s\n05bwYMu7k1Kl5JeWaT/352med/PodbUmRLS7vNsJhxfvx8Or6f/ipuYsAwdEdCVCpKa5ObiiIlua\nXpUEqVlH23G3n3vzL0+qZTXbKx6AS76sy6qTWemNOlKD7cwUW9Njsa/fwtfFGzaVSqo1y2GSA0VK\nFeXKqmoFmTyQi5UcDMhX4ZIKdVIprOdeQVz51qZy2i/Z7ux8WNlZo3iF1CWIpXbG3L/ye8mW+lD3\nmNvSghNiSZGWTM74qF69rg3g7+GH6weu0CGSGCHZ/bQrOo3pxo/l/qnWsgZMzE1wcP4exL1REDjI\np03lkqjcJPWHGcdNpzlhe2S5wfix0ve4slexpK/cfqR6cvqTe/7k4Cn1m9n9l74fMhuvaC8QEAgI\nBIyNQGJ8CkiJkAidO9e6IMAngncRH5uMA1vcef7zp4oXKqiASGK09PzK351w+Yyv3nCun/fj6pnH\ndysIoQlxyTj4122ed+F4WoUPt2vP2DJyN3Bomwdi3sh/kKw3iAwUvmfE1fULr2Di7FZpWkdHJeLu\nreeMoFlIrYzIqsVt88HppOJzv6QGufTvv//By8BorhCr2pAw9mIqo22r/IEO1dbg1P77fPkw1TpS\nmvAm5dDS5dX7lsqNvdfVH5FdiUCraVS/ZgMbNYIj1dGHJ5WHh8Ri9piTnPjbfVANykpjQ8c35MRX\n1YImbRRz8dwqhErVcm1pubHXb14apubZmarqVbyNTuKuzhx6wM97bUb6VTVD41Otm5k0xW5to656\n4s+I043bMKUZDaVcQ/3IwZx8pOf61NdnoP/rNNd13vxmnBB7n6nWpscM3Vtyz7HUp6HzJxcryZ++\nfXrw1HX/6fMvygQCAoF/D4H4xHcgZUci6NES8T7PgnkwsQlJ2Hz0Is8PeBmmDJBIk/vPu+C39Qfh\nwMhs+szR5R5TP72AnacVz8XiEpOY6uMlnnfssnuapkQwXbbLgStDRr2NT1P+uTNobGSaz0iI/Ep2\n66HiBW9+wP7Q+MsUL4IKtmnnFi3rVIa5aU4s2HYcb2IVYzlwwZWR/oqhSfXUH+s3H7sEUokt35Mp\ndPWZhL2Ozmk+d6T+jLV//+ED5v91DPN+7JPGJRFdpfGqFp667omGVcsh3yfCS9kSqS9SUT16zhf4\nKoIpo7dVNpODp9z+lE71JBxuKObthLGqVSxljUS2DK2T2wPVbJ3p1zFxcHnwhCvIqlYiQnIp60I4\ncfW2arbetDF9GWt8qgHru4apnrGuz5DIaPy4eBsndw/p1EQ1BJEWCAgEBAJfBAFSeqQl12nbfuCY\nss/rtzx43q7DJ5V5Se/e4fxVZyxeuwXLGLnzVVi4skxb4szFa1xhUvIbF5+ATbsO8LwjDqm/Q1Lb\nkLAI7Dx0AgtWb8aVm27a3H0VeUT0lFRMpYCI4NmhZROmKKp4nvH6TTRu3r7LFUalOrQ3McmJ0jbF\nmUKuQqhGk2BKc4Znz1+CFGIls2Ek3H7dOkiHfE/L19eoUhF581io5X+JAxo/kWM17eS5S2hctyby\n5U2NaePO/ZwQW7puG5Rr2B672bX0j+ZkUtORxrFcLDWaaT08df4yzydlX1WrVK4MEpOScP7KTdVs\nvWlj+TLm+J48C+Ixa2IsnS8XD8V8sHWT+jBnL2XNXbEBb2Le8jb7jp/h12uz+nX4sZzxybkXuDPx\nRyAgEBAICARkISDIr7JgEpUEAgIBgYB+BEjp9LGbD/bO2oESFUsqK1dqXAVOTP2yKiPckRFhb6z9\ncKYemRPdf+3Nl5yf0XISJ8QqG2kkSC310q7zOLRoHy8hRdNmTNXz4MI9OLvxlLI2EXBpGXtSLqXl\n5Zf2m4ctP29QlmsmiCjr6+qtf/tEUNRsm97jWydvon7XhulthjwF86RpExUcCfO8uVC2duoDfakS\nEYnXDl+BcnUqoEL9SlK2rH14UBj/4piviGLJONVGeQrmRdjT0HR/sVT1ITetiVV6MaAvZi7HbrBr\ncTtGrRmbpls6724OLugwukuaMmNn5DTLidCAV0h4m6DmmsjEiSyPiNxyx9f6+/awKmONtSNWYMe0\nLVg+YAFGrx2Pxn2aqfk2dJCTKYb0/30wVw6e2mQCDszbjefegZh7domaqmvFRpXRdUJPlK9fEVGv\nXmPD6D8wr8sMfNRYbtBY/Ul+9J0/OXhKfjK7/1ruh8yOQ7QXCAgEBAIZRYCUO2m59PULrsCdLSVv\n94nUSUqS2bJnRfirWNiUtuTuaRn4+T+fRqe+9ug3og5WzriAw9s9dHbdtF05nGBLnW9edo3XIdXT\nzn2rYtOSq9j3ZypZg5Qa505wQAwjlpLSqodzILrVWY+njxUKA9o6eHD7JV/qnZZ717WFsaXmM2Jb\nll3HwNH10qi0ki9acp2evxconDuN6/wFzPHi2Zs08ygiz80YfRxV6xRLszQ9kUWHjGuAavWKI4JI\noGNPYXSPPexz+G81/xGhcbjClo7vz3D/EpaR/i6e8kHLzhXShKcPT6rscikAcYxsXaJ0fkwbcQyt\nK65EO/s/sIERkImYSEbYalrYq7fInccE9rXUyRma9eQca8ZuapYdY35rwZRsQ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FJ1JUVT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\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kQPyW4WjHCLnGiIlio3uLrqu8hfMhV77cUrhqezmx0333JiQKdA0WtCmU5kEG5Ue+jGBx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\n1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t9nQLmK5HidTk5GW7hqMyHQ3kZmVhYadx2EOxeOFEn6LeSYZ3AEOAI/DAJ7LJ+SY1nz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\n1awjx2UEZASSIiDmFnMz9By/AOtmjETLBjUwqk97ZcFORDCtYGUhCJes0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Ztjx6ZNiNV4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Rwcv7Fy/ZODp1xbxtr0b/gXH5f450/oOogisnTerDlJpfnLOTDOL7f/WLmZlYD13e9S3Tsv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\n8H92ZVeM31iBRQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run dtc with best parameters, and visualize the tree results. \n",
    "# It isn't helpful to visualize with max_depth=None, so let's just look at a few layers\n",
    "# to see the initial decisions the tree is making.\n",
    "\n",
    "dtc = DecisionTreeClassifier(max_depth=3, max_features=None, min_samples_split=2)\n",
    "dtc.fit(X_train_nosoil, Y_train_nosamp)\n",
    "Y_pred_dtc = dtc.predict(X_test_nosoil)\n",
    "\n",
    "dot_data = tree.export_graphviz(\n",
    "    dtc, out_file=None,\n",
    "    feature_names=X_train_nosoil.columns,\n",
    "    class_names=['Spruce/Fir', 'Lodgepole Pine', 'Ponderosa Pine', 'Cottonwood/Willow', 'Aspen', 'Douglas Fir', 'Krummholz'],\n",
    "    filled=True\n",
    ")\n",
    "graph = pydotplus.graph_from_dot_data(dot_data)\n",
    "Image(graph.create_png())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "This is very cool. Elevation is the primary defining feature, even down into the third layer of the tree. Here's what we're looking at: \n",
    "\n",
    "At the highest elevations (> 3300m), Krummholz is dominant farther from roads, while Spruce/Fir is dominant closer. At slightly lower elevations (between 3050m and 3300m), Spruce/Fir is dominant. Lower down (between 2500m and 3050m), Lodgepole Pine is dominant. At the lowest elevations (< 2500m), Cottonwood/Willow is dominant near water, while Ponderosa Pine is dominant farther away.\n",
    "\n",
    "That's a remarkable amount of information to be able to extract from a model like this! The saturation of the colors in the final nodes correspond to how dominant each tree species is -- the lighter colored boxes have more variation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Analysis\n",
    "\n",
    "Our random forest model performed great -- F1 score of 0.96 on the test set. It appears to have overfit the training data, even using cross validation (average score=0.99). Even so, the precision and recall are remarkable (0.96), considering the best values achieved by the original neural network analysis (0.70).\n",
    "\n",
    "Looking at the individual F1 scores on the test set, the model does slightly better on the common classes (up to 0.97) than on the rare classes (0.86, 0.87).\n",
    "\n",
    "Let's see if we can reduce the overfitting by running the model on that same 'No Soil', unsampled data set we used with KNN and DTC."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Score:  0.948813399294\n",
      "Best Parameters:  {'max_features': None, 'min_samples_split': 2, 'n_estimators': 64}\n",
      "Confusion Matrix: \n",
      "\n",
      " [[60805  2621     3     0    23     5   139]\n",
      " [ 2198 82346   156     2   127   102    26]\n",
      " [    0   252 10143    56     5   223     0]\n",
      " [    0     0    89   698     0    29     0]\n",
      " [   34   524    21     0  2301     9     0]\n",
      " [    3   166   341    28     4  4688     0]\n",
      " [  313    42     0     0     1     0  5781]] \n",
      "\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.96      0.96      0.96     63596\n",
      "          2       0.96      0.97      0.96     84957\n",
      "          3       0.94      0.95      0.95     10679\n",
      "          4       0.89      0.86      0.87       816\n",
      "          5       0.93      0.80      0.86      2889\n",
      "          6       0.93      0.90      0.91      5230\n",
      "          7       0.97      0.94      0.96      6137\n",
      "\n",
      "avg / total       0.96      0.96      0.96    174304\n",
      "\n",
      "1:32:49.875336\n"
     ]
    }
   ],
   "source": [
    "# Run rfc with best parameters, but using unsampled 'No Soil' features to reduce overfitting.\n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "rfc_params = {'n_estimators':[24, 64],\n",
    "              'min_samples_split':[2, 8],\n",
    "              'max_features':['sqrt', None]}\n",
    "rfc_grid = GridSearchCV(RandomForestClassifier(), rfc_params, cv=4)\n",
    "rfc_grid.fit(X_train_nosoil, Y_train_nosamp)\n",
    "print('Best Score: ', rfc_grid.best_score_)\n",
    "print('Best Parameters: ', rfc_grid.best_params_)\n",
    "\n",
    "Y_pred_rfc = rfc_grid.predict(X_test_nosoil)\n",
    "print('Confusion Matrix: \\n\\n', confusion_matrix(Y_test_nosamp, Y_pred_rfc), '\\n')\n",
    "print('Classification Report: \\n\\n', classification_report(Y_test_nosamp, Y_pred_rfc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our Random Forest results with the unsampled, 'No Soil' data set have the same F1 score (0.96) as with the full, resampled set, but without the overfitting on the training set. Our training set accuracy is 0.95. This is a good result, and Random Forest continues to be one of the best models for this data set. It remains just barely less precise than KNN (F1 score of 0.97).\n",
    "\n",
    "As one final piece of analysis, we can see which class types are the most difficult to discern from one another. The most false classifications come at the intersections of classes 2 and 5 (Lodgepole Pine and Aspen), and classes 3 and 6 (Ponderosa Pine and Doug Fir). Class 4 (Cottonwood/Willow) is the rarest, and a large fraction were misclassified as class 3 (Ponderosa Pine). This makes sense given our Decision Tree analysis that these two classes occur at the same elevation. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Gradient Boosting Analysis\n",
    "\n",
    "Gradient boosting took too long to run to be feasible. After hours of running the whole training set, I ended up running gridsearch with only 10% of the data. Even with that small percentage of the data, we did quite well -- F1 score of 0.88. The parameter choices for the model were those which allowed it to do the most work -- high n_estimators, high learning_rate, and high max_depth. This suggests there's even more accuracy to be gained from running the model with larger parameters (which take longer). Since we've had good success with our unsampled, 'No Soil' data set, let's try running GBC one more time with that, and hopefully it will run faster."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross Val Score: \n",
      "\n",
      " [ 0.90269377  0.9010199   0.90327213  0.90269978]\n",
      "\n",
      "Confusion Matrix: \n",
      "\n",
      " [[56746  6621     7     0    41    15   166]\n",
      " [ 5567 78628   326     1   225   176    34]\n",
      " [    3   585  9696    67     6   322     0]\n",
      " [    0     2    90   688     0    36     0]\n",
      " [   46   748    21     0  2067     7     0]\n",
      " [   13   370   387    36     2  4422     0]\n",
      " [  421    48     0     0     1     0  5667]]\n",
      "\n",
      "Classification Report: \n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "          1       0.90      0.89      0.90     63596\n",
      "          2       0.90      0.93      0.91     84957\n",
      "          3       0.92      0.91      0.91     10679\n",
      "          4       0.87      0.84      0.86       816\n",
      "          5       0.88      0.72      0.79      2889\n",
      "          6       0.89      0.85      0.87      5230\n",
      "          7       0.97      0.92      0.94      6137\n",
      "\n",
      "avg / total       0.91      0.91      0.91    174304\n",
      "\n",
      "4:27:19.469984\n"
     ]
    }
   ],
   "source": [
    "# Gradient Boosting with \"No Soil\" feature selection and unsampled set. \n",
    "\n",
    "tic = t_now()\n",
    "\n",
    "gbc = GradientBoostingClassifier(n_estimators=200, learning_rate=0.3, max_depth=6)\n",
    "gbc.fit(X_train_nosoil, Y_train_nosamp)\n",
    "Y_pred_gbc = gbc.predict(X_test_nosoil)\n",
    "\n",
    "print('Cross Val Score: \\n\\n', cross_val_score(gbc, X_train_nosoil, Y_train_nosamp, cv=4))\n",
    "print('\\nConfusion Matrix: \\n\\n', confusion_matrix(Y_test_nosamp, Y_pred_gbc))\n",
    "print('\\nClassification Report: \\n\\n', classification_report(Y_test_nosamp, Y_pred_gbc))\n",
    "\n",
    "print(t_dif(tic))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using gradient boosting with the unsampled, 'No Soil' data set, and using the best parameters from tuning with 10% of the data set, we are able to achieve a cross-val score of 0.90 and an F1 score of 0.91 on the test set. This is a pretty good result, but it took 4.5 hours to run a single pass. In contrast, a single decision tree was able to achieve the same score on the test set after 8 minutes of training. Even though we may be able to improve accuracy by further parameter tuning or by increasing n_estimators, the model is not effective for this data set when we take processing time into account. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "Our best models (KNN and Random Forest) were able to perform significantly better than those used in the original 1998 analysis. The best results were on a data set with no resampling, and in fact this was a necessary step in reducing overfitting. KNN was the top scorer, with 0.96 on training cross validation and 0.97 F1 score on the test set, averaging over the seven classes. Random Forest was second, scoring 0.95 on cross validation and 0.96 on F1.\n",
    "\n",
    "Gradient Boosting also did well (best F1 score: 0.91), however, processing time was very long, which limited parameter tuning. Decision Tree performed well compared to the 1998 results, but wasn't quite as accurate as KNN or Random Forest (best F1 score of 0.93). It did allow us to understand what kinds of choices our model was making near the top of the tree, giving real-world meaning to our model.\n",
    "\n",
    "Our linear models all performed about as well as the linear model from the 1998 analysis, topping out at F1 score of 0.60. This suggests that the data set is not well described by a linear model. We were forced to abandon SVC with kernels in favor of LinearSVC due to long processing times. Naive Bayes performed poorly, as the problem is not framed as binary features.\n",
    "\n",
    "Perhaps most importantly, we were able to completely remove the Soil Type and Wilderness Area features from processing with negligible effect on model results, especially for KNN. This significantly simplifies the data collection required for possibly expanding this analysis to other forests in the region (although in the next section we'll explore why not). Specifically, all the necessary numeric features could be drawn from a map and digital elevation model using simple GIS tools. \n",
    "\n",
    "#### Why is KNN So Accurate?\n",
    "\n",
    "Finally, why is KNN performing so well? Primarily because it is an excellent interpolative model, and that's exactly what we're asking it to do in this analysis. The data set we're pulling from here is a real-world sample of forest types. Forests (in wilderness areas) are continuous, and specific types of trees grow together in groves. So in the real world, the forest type most likely to be adjacent to you in is that same type you're already in. This is a very tangible way of thinking about KNN. If you want to determine what species a specific tree is without looking at it, a great way to do it is to identify the three trees closest to you. Your tree is likely to be the same kind. \n",
    "\n",
    "Because our test data is randomly sampled from the full, continuous data set, we're essentially using the model to fill back in those gaps that we've extracted. And because we have so many numeric variables (like 'Distance to Road', which we wouldn't actually expect to be predictive in a forest more generally), we can effectively describe every physical location in the data region with a unique combination of those features. This is not generalizable outside the boundaries we're starting with. Decision Tree and Random Forest are doing the same thing, just by different means.\n",
    "\n",
    "This fact severely limits the scope of our results. We've created an excellent method for interpolating forest cover type in a data set we already know a lot about. Expanding beyond these four wilderness areas in Colorado would mean including a lot more data. In a sense, the most powerful result we've obtained from this analysis is actually the visualization of the top layers of the Decision Tree, not the impressive accuracy of the KNN model. By examining the Decision Tree, we were able to see how forest cover dominance is affected by elevation and proximity to water. This begs the question: Could our linear model, with 60% accuracy, actually be the most predictive and generalizable?\n",
    "\n",
    "For further research on this data set, I would recommend a deeper analysis of the Decision Tree model in order to gain insight about why certain types of forest grow where they do. Also, by obtaining substantially more data, we may be able to move from simply an interpolative model to something more general, to something that has application in regions it hasn't necessarily seen before. \n",
    "\n",
    "Thanks for reading!"
   ]
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